├── .gitignore ├── LICENSE ├── README.md ├── math-symbols.md ├── playground ├── multi-agent │ └── README.md └── single-agent │ └── README.md ├── quantum-symbols.md └── resources └── Machine Learning for OpenCV ├── Chapter01 ├── Old │ └── code-reviewer-instructions.md └── code-reviewer-instructions.md ├── Chapter02 ├── Old │ └── code-reviewer-instructions.md └── code-reviewer-instructions.md ├── Chapter03 ├── New │ └── code-reviewer-instructions.md └── code-reviewer-instructions.md ├── Chapter04 └── code-reviewer-instructions.md ├── Chapter05 ├── 05.00-Using-Decision-Trees-to-Make-a-Medical-Diagnosis.ipynb ├── 05.01-Building-Our-First-Decision-Tree.ipynb ├── 05.02-Using-Decision-Trees-to-Diagnose-Breast-Cancer.ipynb └── 05.03-Using-Decision-Trees-for-Regression.ipynb ├── Chapter06 └── code-reviewer-instructions.md ├── Chapter07 └── code-reviewer-instructions.md ├── Chapter08 └── code-reviewer-instructions.md ├── Chapter09 └── code-reviewer-instructions.md ├── Chapter10 └── code-reviewer-instructions.md ├── Chapter11 └── code-reviewer-instructions.md ├── Chapter12 └── code-reviewer-instructions.md └── Software Hardware list.pdf /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | The MIT License (MIT) 2 | 3 | Copyright (c) 2018 luckyzhou 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy of 6 | this software and associated documentation files (the "Software"), to deal in 7 | the Software without restriction, including without limitation the rights to 8 | use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 9 | the Software, and to permit persons to whom the Software is furnished to do so, 10 | subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 17 | FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 18 | COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 19 | IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 20 | CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 21 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome AI books 2 | 3 | Some awesome AI related books and pdfs for downloading and learning. 4 | 5 | ## Preface 6 | 7 | **This repo only used for learning, do not use in business.** 8 | 9 | Welcome for providing great books in this repo or tell me which great book you need and I will try to append it in this repo, any idea you can create issue or PR here. 10 | 11 | Due to github Large file storage limition, all books pdf stored in **Yandex.Disk**. 12 | 13 | Some often used **Mathematic Symbols** can refer this [page](https://github.com/zslucky/awesome-AI-books/blob/master/math-symbols.md) 14 | 15 | ## Content 16 | - [Organization with papers/researchs](https://github.com/zslucky/awesome-AI-books#organization-with-papersresearchs) 17 | - [Training ground](https://github.com/zslucky/awesome-AI-books#training-ground) 18 | - [Books](https://github.com/zslucky/awesome-AI-books#books) 19 | - [Introductory theory and get start](https://github.com/zslucky/awesome-AI-books#introductory-theory-and-get-start) 20 | - [Mathematics](https://github.com/zslucky/awesome-AI-books#mathematics) 21 | - [Data mining](https://github.com/zslucky/awesome-AI-books#data-mining) 22 | - [Deep Learning](https://github.com/zslucky/awesome-AI-books#deep-learning) 23 | - [Philosophy](https://github.com/zslucky/awesome-AI-books#philosophy) 24 | - [Quantum with AI](https://github.com/zslucky/awesome-AI-books#quantum-with-ai) 25 | - [Quantum Basic](https://github.com/zslucky/awesome-AI-books#quantum-basic) 26 | - [Quantum AI](https://github.com/zslucky/awesome-AI-books#quantum-ai) 27 | - [Quantum Related Framework](https://github.com/zslucky/awesome-AI-books#quantum-related-framework) 28 | - [Libs With Online Books](https://github.com/zslucky/awesome-AI-books#libs-with-online-books) 29 | - [Reinforcement Learning](https://github.com/zslucky/awesome-AI-books#reinforcement-learning) 30 | - [Feature Selection](https://github.com/zslucky/awesome-AI-books#feature-selection) 31 | - [Machine Learning](https://github.com/zslucky/awesome-AI-books#machine-learning-1) 32 | - [Deep Learning](https://github.com/zslucky/awesome-AI-books#deep-learning-1) 33 | - [NLP](https://github.com/zslucky/awesome-AI-books#nlp) 34 | - [CV](https://github.com/zslucky/awesome-AI-books#cv) 35 | - [Meta Learning](https://github.com/zslucky/awesome-AI-books#meta-learning) 36 | - [Transfer Learning](https://github.com/zslucky/awesome-AI-books#transfer-learning) 37 | - [Auto ML](https://github.com/zslucky/awesome-AI-books#auto-ml) 38 | - [Dimensionality Reduction](https://github.com/zslucky/awesome-AI-books#dimensionality-reduction) 39 | - [Distributed training](https://github.com/zslucky/awesome-AI-books#distributed-training) 40 | 41 | ## Organization with papers/researchs 42 | 43 | - [arxiv.org](https://arxiv.org/) 44 | - [Science](http://www.sciencemag.org/) 45 | - [Nature](https://www.nature.com/nature/) 46 | - [DeepMind Publications](https://deepmind.com/research/publications/) 47 | - [OpenAI Research](https://openai.com/research/) 48 | 49 | ## Training ground 50 | 51 | - [OpenAI Gym](https://gym.openai.com/): A toolkit for developing and comparing reinforcement learning algorithms. (Can play with [Atari](https://en.wikipedia.org/wiki/Atari), Box2d, MuJoCo etc...) 52 | - [malmo](https://github.com/Microsoft/malmo): Project Malmö is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. 53 | - [DeepMind Pysc2](https://github.com/deepmind/pysc2): StarCraft II Learning Environment. 54 | - [Procgen](https://github.com/openai/procgen): Procgen Benchmark: Procedurally-Generated Game-Like Gym-Environments. 55 | - [TorchCraftAI](https://torchcraft.github.io/TorchCraftAI/): A bot platform for machine learning research on StarCraft®: Brood War® 56 | - [Valve Dota2](https://developer.valvesoftware.com/wiki/Dota_Bot_Scripting): Dota2 game acessing api. ([CN doc](https://developer.valvesoftware.com/wiki/Dota_Bot_Scripting:zh-cn)) 57 | - [Mario AI Framework](https://github.com/amidos2006/Mario-AI-Framework): A Mario AI framework for using AI methods. 58 | - [Google Dopamine](https://github.com/google/dopamine): Dopamine is a research framework for fast prototyping of reinforcement learning algorithms 59 | - [TextWorld](https://github.com/Microsoft/TextWorld): Microsoft - A learning environment sandbox for training and testing reinforcement learning (RL) agents on text-based games. 60 | - [Mini Grid](https://github.com/maximecb/gym-minigrid): Minimalistic gridworld environment for OpenAI Gym 61 | - [MAgent](https://github.com/geek-ai/MAgent): A Platform for Many-agent Reinforcement Learning 62 | - [XWorld](https://github.com/PaddlePaddle/XWorld): A C++/Python simulator package for reinforcement learning 63 | - [Neural MMO](https://github.com/openai/neural-mmo): A Massively Multiagent Game Environment 64 | - [MinAtar](https://github.com/kenjyoung/MinAtar): MinAtar is a testbed for AI agents which implements miniaturized version of several Atari 2600 games. 65 | - [craft-env](https://github.com/Feryal/craft-env): CraftEnv is a 2D crafting environment 66 | - [gym-sokoban](https://github.com/mpSchrader/gym-sokoban): Sokoban is Japanese for warehouse keeper and a traditional video game 67 | - [Pommerman](https://github.com/MultiAgentLearning/playground) Playground hosts Pommerman, a clone of Bomberman built for AI research. 68 | - [gym-miniworld](https://github.com/maximecb/gym-miniworld#introduction) MiniWorld is a minimalistic 3D interior environment simulator for reinforcement learning & robotics research 69 | - [vizdoomgym](https://github.com/shakenes/vizdoomgym) OpenAI Gym wrapper for [ViZDoom](https://github.com/mwydmuch/ViZDoom) (A Doom-based AI Research Platform for Reinforcement Learning from Raw Visual Information) enviroments. 70 | - [ddz-ai](https://github.com/freefuiiismyname/ddz-ai) 以孤立语假设和宽度优先搜索为基础，构建了一种多通道堆叠注意力Transformer结构的斗地主ai 71 | 72 | 73 | ## Books 74 | 75 | ### Introductory theory and get start 76 | 77 | - [Artificial Intelligence-A Modern Approach (3rd Edition)](https://yadi.sk/i/G6NlUUV8SAVimg) - Stuart Russell & peter Norvig 78 | - **COMMERCIAL** [Grokking Artificial Intelligence Algorithms](https://www.manning.com/books/grokking-artificial-intelligence-algorithms) - Rishal Hurbans 79 | 80 | ### Mathematics 81 | 82 | - [A First Course in ProbabilityA First Course in Probability (8th)](https://yadi.sk/i/aDvGdqWlcXxbhQ) - Sheldon M Ross 83 | - [Convex Optimization](https://yadi.sk/i/9KGVXuFJs3kakg) - Stephen Boyd 84 | - [Elements of Information Theory Elements](https://yadi.sk/i/2YWnNsAeBc9qcA) - Thomas Cover & Jay A Thomas 85 | - [Discrete Mathematics and Its Applications 7th](https://yadi.sk/i/-r3jD4gB-8jn1A) - Kenneth H. Rosen 86 | - [Introduction to Linear Algebra (5th)](http://www.mediafire.com/file/f31dl0ghup7e6gk/Introduction_to_Linear_Algebra_5th_-_Gilbert_Strang.pdf/file) - Gilbert Strang 87 | - [Linear Algebra and Its Applications (5th)](https://yadi.sk/i/uWEQVrCquqw1Ug) - David C Lay 88 | - [Probability Theory The Logic of Science](https://yadi.sk/i/TKQYNPSKGNbdUw) - Edwin Thompson Jaynes 89 | - [Probability and Statistics 4th](https://yadi.sk/i/38jrMmEXnJQZqg) - Morris H. DeGroot 90 | - [Statistical Inference (2nd)](https://yadi.sk/i/HWrbKYrYdpNMYw) - Roger Casella 91 | - [信息论基础 (原书Elements of Information Theory Elements第2版)](https://yadi.sk/i/HqGOyAkRCxCwIQ) - Thomas Cover & Jay A Thomas 92 | - [凸优化 (原书Convex Optimization)](https://yadi.sk/i/zUPPAi58v1gfkw) - Stephen Boyd 93 | - [数理统计学教程](https://yadi.sk/i/ikuXCrNgRCEVnw) - 陈希儒 94 | - [数学之美 2th](https://yadi.sk/i/QJPxzK4ZBuF8iQ) - 吴军 95 | - [概率论基础教程 (原书A First Course in ProbabilityA First Course in Probability第9版)](https://yadi.sk/i/wQZQ80UFLFZ48w) - Sheldon M Ross 96 | - [线性代数及其应用 (原书Linear Algebra and Its Applications第3版)](https://yadi.sk/i/cNNBS4eaLleR3g) - David C Lay 97 | - [统计推断 (原书Statistical Inference第二版)](https://yadi.sk/i/ksHAFRUSaoyk9g) - Roger Casella 98 | - [离散数学及其应用 (原书Discrete Mathematics and Its Applications第7版)](https://yadi.sk/i/kJHMmMA4ot66bw) - Kenneth H.Rosen 99 | 100 | ### Data mining 101 | 102 | - [Introduction to Data Mining](https://yadi.sk/i/H7wc_FaMDl9QXQ) - Pang-Ning Tan 103 | - [Programming Collective Intelligence](https://yadi.sk/i/YTjrJWu7kXVrGQ) - Toby Segaran 104 | - [Feature Engineering for Machine Learning](https://yadi.sk/i/WiO7lageMIuIfg) - Amanda Casari, Alice Zheng 105 | - [集体智慧编程](https://yadi.sk/i/0DW5reTrXQ6peQ) - Toby Segaran 106 | 107 | ### Machine Learning 108 | 109 | - [Information Theory, Inference and Learning Algorithms](https://yadi.sk/i/JXYto8yE6PJO8Q) - David J C MacKay 110 | - [Machine Learning](https://yadi.sk/i/03Jg9WMzgD2YlA) - Tom M. Mitchell 111 | - [Pattern Recognition and Machine Learning](https://yadi.sk/i/8ffTCaMH0bM8uQ) - Christopher Bishop 112 | - [The Elements of Statistical Learning](https://yadi.sk/i/hfatiRyBCwfcWw) - Trevor Hastie 113 | - [Machine Learning for OpenCV](https://yadi.sk/i/_UdlHqwuR-Wdxg) - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV)) 114 | - [机器学习](https://yadi.sk/i/vfoPTRRfgtEQKA) - 周志华 115 | - [机器学习 (原书Machine Learning)](https://yadi.sk/i/jTNv4kzG-lmlYQ) - Tom M. Mitchell 116 | - [统计学习方法](https://yadi.sk/i/R08dbDMOJb3KKw) - 李航 117 | 118 | ### Deep Learning 119 | - Online Quick learning 120 | - [Dive into Deep Learning](https://d2l.ai/) - (Using MXNet)An interactive deep learning book with code, math, and discussions. 121 | - [d2l-pytorch](https://github.com/dsgiitr/d2l-pytorch) - (Dive into Deep Learning) pytorch version. 122 | - [动手学深度学习](https://zh.d2l.ai/) - (Dive into Deep Learning) for chinese. 123 | - [Deep Learning](https://yadi.sk/i/2fOK_Xib-JlocQ) - Ian Goodfellow & Yoshua Bengio & Aaron Courville 124 | - [Deep Learning Methods and Applications](https://yadi.sk/i/uQAWfeKVmenmkg) - Li Deng & Dong Yu 125 | - [Learning Deep Architectures for AI](https://yadi.sk/i/AWpRq2hSB9RmoQ) - Yoshua Bengio 126 | - [Machine Learning An Algorithmic Perspective (2nd)](https://yadi.sk/i/1gOQ-Y5r4uP6Kw) - Stephen Marsland 127 | - [Neural Network Design (2nd)](https://yadi.sk/i/5LLMPfNcuaPTvQ) - Martin Hagan 128 | - [Neural Networks and Learning Machines (3rd)](https://yadi.sk/i/6s9AauRP1OGT2Q) - Simon Haykin 129 | - [Neural Networks for Applied Sciences and Engineering](https://yadi.sk/i/JK7aj5TsmoC1dA) - Sandhya Samarasinghe 130 | - [深度学习 (原书Deep Learning)](https://yadi.sk/i/DzzZU_QPosSTBQ) - Ian Goodfellow & Yoshua Bengio & Aaron Courville 131 | - [神经网络与机器学习 (原书Neural Networks and Learning Machines)](https://yadi.sk/i/ogQff9JpLEdHMg) - Simon Haykin 132 | - [神经网络设计 (原书Neural Network Design)](https://yadi.sk/i/uR2OAHHgnZHUuw) - Martin Hagan 133 | - **COMMERCIAL** [Interpretable AI](https://www.manning.com/books/interpretable-ai) - Ajay Thampi 134 | - **COMMERCIAL** [Conversational AI](https://www.manning.com/books/conversational-ai) - Andrew R. Freed 135 | 136 | ### Philosophy 137 | - **COMMERCIAL** [Human Compatible: Artificial Intelligence and the Problem of Control](https://www.amazon.com/Human-Compatible-Artificial-Intelligence-Problem-ebook/dp/B07N5J5FTS) - Stuart Russell 138 | - **COMMERCIAL** [Life 3.0: Being Human in the Age of Artificial Intelligence](https://www.amazon.com/Life-3-0-Being-Artificial-Intelligence/dp/1101946598) - Max Tegmark 139 | - **COMMERCIAL** [Superintelligence: Paths, Dangers, Strategies](https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834/ref=pd_sbs_14_t_0/146-0357100-6717505?_encoding=UTF8&pd_rd_i=0198739834&pd_rd_r=676ace91-552c-4865-a8d3-6273db5418bf&pd_rd_w=zYEu2&pd_rd_wg=hQdGQ&pf_rd_p=5cfcfe89-300f-47d2-b1ad-a4e27203a02a&pf_rd_r=DTH77KT4FSVRMJ47GBVQ&psc=1&refRID=DTH77KT4FSVRMJ47GBVQ) - Nick Bostrom 140 | 141 | ## Quantum with AI 142 | 143 | - #### Quantum Basic 144 | - [Quantum Computing Primer](https://www.dwavesys.com/tutorials/background-reading-series/quantum-computing-primer#h1-0) - D-Wave quantum computing primer 145 | - [Quantum computing 101](https://uwaterloo.ca/institute-for-quantum-computing/quantum-computing-101) - Quantum computing 101, from University of Waterloo 146 | - [pdf](https://yadi.sk/i/0VCfWmb3HrrPuw) Quantum Computation and Quantum Information - Nielsen 147 | - [pdf](https://yadi.sk/i/mHoyVef8RaG0aA) 量子计算和量子信息（量子计算部分）- Nielsen 148 | - #### Quantum AI 149 | - [Quantum neural networks](http://axon.cs.byu.edu/papers/ezhov.fdisis00.pdf) 150 | - [An Artificial Neuron Implemented on an Actual Quantum Processor](https://arxiv.org/pdf/1811.02266.pdf) 151 | - [Classification with Quantum Neural Networks on Near Term Processors](https://arxiv.org/pdf/1802.06002.pdf) 152 | - [Black Holes as Brains: Neural Networks with Area Law Entropy](https://arxiv.org/pdf/1801.03918.pdf) 153 | - #### Quantum Related Framework 154 | - [ProjectQ](https://github.com/ProjectQ-Framework/ProjectQ) - ProjectQ is an open source effort for quantum computing. 155 | 156 | ## Libs With Online Books 157 | - #### GC (Generative Content) 158 | - [Stable Diffusion](https://github.com/CompVis/stable-diffusion) - [[Paper](https://arxiv.org/abs/2112.10752)] A latent text-to-image diffusion model 159 | - [Stable Diffusion V2](https://github.com/Stability-AI/stablediffusion) - High-Resolution Image Synthesis with Latent Diffusion Models 160 | - [GFPGAN](https://github.com/TencentARC/GFPGAN) - [[Paper](https://arxiv.org/abs/2101.04061)] GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration. 161 | - [ESRGAN](https://github.com/xinntao/ESRGAN) - [[Paper](https://arxiv.org/abs/2107.10833)] ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR. 162 | - [CodeFormer](https://github.com/sczhou/CodeFormer) - [[Paper](https://arxiv.org/abs/2206.11253)] - [NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer 163 | - [UniPC](https://github.com/wl-zhao/UniPC) - [[Paper](https://arxiv.org/abs/2302.04867)] UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models 164 | - #### Reinforcement Learning 165 | - [A3C](https://arxiv.org/pdf/1602.01783.pdf) - Google DeepMind Asynchronous Advantage Actor-Critic algorithm 166 | - [Q-Learning](http://www.gatsby.ucl.ac.uk/~dayan/papers/cjch.pdf) SARSA [DQN](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) [DDQN](https://arxiv.org/pdf/1509.06461.pdf) - Q-Learning is a value-based Reinforcement Learning algorithm 167 | - [DDPG](https://arxiv.org/pdf/1509.02971.pdf) - Deep Deterministic Policy Gradient, 168 | - [Large-Scale Curiosity](https://arxiv.org/pdf/1808.04355.pdf) - Large-Scale Study of Curiosity-Driven Learning 169 | - [PPO](https://arxiv.org/pdf/1707.06347.pdf) - OpenAI Proximal Policy Optimization Algorithms 170 | - [RND](https://arxiv.org/pdf/1810.12894.pdf) - OpenAI Random Network Distillation, an exploration bonus for deep reinforcement learning method. 171 | - [VIME](https://arxiv.org/pdf/1605.09674.pdf) - OpenAI Variational Information Maximizing Exploration 172 | - [DQV](https://arxiv.org/pdf/1810.00368.pdf) - Deep Quality-Value (DQV) Learning 173 | - [ERL](https://arxiv.org/pdf/1805.07917.pdf) - Evolution-Guided Policy Gradient in Reinforcement Learning 174 | - [MF Multi-Agent RL](https://arxiv.org/pdf/1802.05438.pdf) - Mean Field Multi-Agent Reinforcement Learning. (this paper include MF-Q and MF-AC) 175 | - [MAAC](https://arxiv.org/pdf/1810.02912.pdf) - Actor-Attention-Critic for Multi-Agent Reinforcement Learning 176 | - #### Feature Selection 177 | - [scikit-feature](http://featureselection.asu.edu/algorithms.php) - A collection of feature selection algorithms, available on [Github](https://github.com/jundongl/scikit-feature) 178 | - #### Machine Learning 179 | - [Scikit learn](https://scikit-learn.org/stable/) (**Python**) - Machine Learning in Python. 180 | - [Linfa](https://github.com/rust-ml/linfa) (**Rust**) - spirit of `scikit learn`, a rust ML lib. 181 | - [Xgboost](https://xgboost.readthedocs.io/en/latest/tutorials/model.html) (**Python, R, JVM, Julia, CLI**) - Xgboost lib's document. 182 | - [LightGBM](https://lightgbm.readthedocs.io/en/latest/Features.html#) (**Python, R, CLI**) - Microsoft lightGBM lib's features document. 183 | - [CatBoost](https://arxiv.org/pdf/1706.09516.pdf) (**Python, R, CLI**) - Yandex Catboost lib's key algorithm pdf papper. 184 | - [StackNet](https://github.com/kaz-Anova/StackNet) (**Java, CLI**) - Some model stacking algorithms implemented in this lib. 185 | - [RGF](https://arxiv.org/pdf/1109.0887.pdf) - Learning Nonlinear Functions Using `Regularized Greedy Forest` (multi-core implementation [FastRGF](https://github.com/RGF-team/rgf/tree/master/FastRGF)) 186 | - [FM](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf), [FastFM](https://arxiv.org/pdf/1505.00641.pdf), [FFM](https://arxiv.org/pdf/1701.04099.pdf), [XDeepFM](https://arxiv.org/pdf/1803.05170.pdf) - Factorization Machines and some extended Algorithms 187 | - #### Deep Learning 188 | - [GNN Papers](https://github.com/thunlp/GNNPapers) - Must-read papers on graph neural networks (GNN) 189 | - [EfficientNet](https://arxiv.org/pdf/1905.11946.pdf) - Rethinking Model Scaling for Convolutional Neural Networks 190 | - [DenseNet](https://arxiv.org/pdf/1608.06993.pdf) - Densely Connected Convolutional Networks 191 | - #### NLP 192 | - [XLNet](https://arxiv.org/pdf/1906.08237.pdf) - [repo](https://github.com/zihangdai/xlnet) XLNet: Generalized Autoregressive Pretraining for Language Understanding 193 | - [BERT](https://arxiv.org/pdf/1810.04805.pdf) - Pre-training of Deep Bidirectional Transformers for Language Understanding 194 | - [GPT-3](https://arxiv.org/pdf/2005.14165.pdf) - Language Models are Few-Shot Learners 195 | - #### CV 196 | - [Fast R-CNN](https://arxiv.org/pdf/1504.08083.pdf) - Fast Region-based Convolutional Network method (Fast R-CNN) for object detection 197 | - [Mask R-CNN](https://arxiv.org/pdf/1703.06870.pdf) - Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. 198 | - [GQN](http://science.sciencemag.org/content/360/6394/1204/tab-pdf) - DeepMind Generative Query Network, Neural scene representation and rendering 199 | - #### Meta Learning 200 | - [MAML](https://arxiv.org/pdf/1703.03400.pdf) - Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks 201 | - #### Transfer Learning 202 | - [GCN](https://arxiv.org/pdf/1803.08035.pdf) - Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs 203 | - #### Auto ML 204 | - [Model Search](https://github.com/google/model_search) (**Python**) - Google Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale. 205 | - [TPOT](https://github.com/EpistasisLab/tpot) (**Python**) - TPOT is a lib for AutoML. 206 | - [Auto-sklearn](https://automl.github.io/auto-sklearn/master/) (**Python**) - auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator 207 | - [Auto-Keras](https://autokeras.com/) (**Python**) - Auto-Keras is an open source software library for automated machine learning (AutoML). It is developed by DATA Lab 208 | - [TransmogrifAI](https://docs.transmogrif.ai/en/stable/index.html) (**JVM**) - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Spark 209 | - [Auto-WEKAA](http://www.cs.ubc.ca/labs/beta/Projects/autoweka/) - Provides automatic selection of models and hyperparameters for [WEKA](https://www.cs.waikato.ac.nz/ml/weka/). 210 | - [MLBox](https://github.com/AxeldeRomblay/MLBox) (**Python**) - MLBox is a powerful Automated Machine Learning python library 211 | - #### Pipeline Training 212 | - [ZenML](https://github.com/maiot-io/zenml) (**Python**) - ZenML is built for ML practitioners who are ramping up their ML workflows towards production 213 | - #### Dimensionality Reduction 214 | - [t-SNE](http://www.cs.toronto.edu/~hinton/absps/tsne.pdf) (**Non-linear/Non-params**) - T-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for visualization 215 | - [PCA](https://www.cs.cmu.edu/~elaw/papers/pca.pdf) (**Linear**) - Principal component analysis 216 | - [LDA](https://www.isip.piconepress.com/publications/reports/1998/isip/lda/lda_theory.pdf) (**Linear**) - Linear Discriminant Analysis 217 | - [LLE](https://cs.nyu.edu/~roweis/lle/papers/lleintro.pdf) (**Non-linear**) - Locally linear embedding 218 | - [Laplacian Eigenmaps](http://web.cse.ohio-state.edu/~belkin.8/papers/LEM_NC_03.pdf) - Laplacian Eigenmaps for Dimensionality Reduction and Data Representation 219 | - [Sammon Mapping](http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV0910/henderson.pdf) (**Non-linear**) - Sammon mapping is designed to minimise the differences between corresponding inter-point distances in the 220 | two spaces 221 | - #### Data Processing 222 | - [Pandas](https://github.com/pandas-dev/pandas) (**Python**) - Flexible and powerful data analysis / manipulation library for Python. 223 | - [Polars](https://github.com/pola-rs/polars) (**Rust, Python**) - Lightning-fast DataFrame library for Rust and Python. 224 | 225 | ## Distributed training 226 | - [Horovod](https://github.com/horovod/horovod#usage) - Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed Deep Learning fast and easy to use. 227 | - [Acme](https://github.com/deepmind/acme) - A Research Framework for (Distributed) Reinforcement Learning. 228 | - [bagua](https://github.com/BaguaSys/bagua) - Bagua is a flexible and performant distributed training algorithm development framework. 229 | 230 | ## Support this project 231 | ![btc-clean-qrcode](https://user-images.githubusercontent.com/15725589/152709449-f6b7174b-2990-43f6-ac69-c8549fe7310c.png) 232 | ![eth-clean-qrcode](https://user-images.githubusercontent.com/15725589/152709451-6c2691f9-dec7-4b60-9d20-9fdded828c8c.png) 233 | 234 | 235 | ## Contributors 236 | 237 | ### Code Contributors 238 | 239 | This project exists thanks to all the people who contribute. [[Contribute](CONTRIBUTING.md)]. 240 | 241 | 242 | ### Financial Contributors 243 | 244 | Become a financial contributor and help us sustain our community. [[Contribute](https://opencollective.com/awesome-AI-books/contribute)] 245 | 246 | #### Individuals 247 | 248 | 249 | 250 | #### Organizations 251 | 252 | Support this project with your organization. Your logo will show up here with a link to your website. [[Contribute](https://opencollective.com/awesome-AI-books/contribute)] 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | -------------------------------------------------------------------------------- /math-symbols.md: -------------------------------------------------------------------------------- 1 | # Mathematic Symbols 2 | 3 | Some often used Mathematic symbols' check list :mortar_board: 4 | 5 | ## Numbers 6 | 7 | Symbol | Meaning | Symbol | Meaning 8 | ------ | ------- | ------ | ------- 9 | ℝ | Real numbers (实数) | ℚ | Rational numbers (有理数) 10 | ℤ | Integers (整数) | ℕ | Natural numbers (自然数) 11 | ℂ | Complex number (复数) | 12 | 13 | -------------------------------------------------------------------------------- /playground/multi-agent/README.md: -------------------------------------------------------------------------------- 1 | # Multi-Agent Playground 2 | 3 | All tasks in this folder will show you the collaboration between multi agents. -------------------------------------------------------------------------------- /playground/single-agent/README.md: -------------------------------------------------------------------------------- 1 | # Single-Agent Playground 2 | 3 | All tasks in this folder will show you how single agent works. -------------------------------------------------------------------------------- /quantum-symbols.md: -------------------------------------------------------------------------------- 1 | # Quantum Symbols 2 | 3 | Some often used Quantum symbols' check list :mortar_board: -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter01/Old/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | All you have to do is go to: 36 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 37 | 38 | However, at time of writing this (3/2/2017), MyBinder seems to be down, which 39 | probably has to do with the recent outtage of Amazon AWS: 40 | http://mybinder.org/status 41 | 42 | I hope it will be up again soon. 43 | 44 | 45 | 46 | ### Using Jupyter notebook 47 | 48 | You basically want to follow the installation instructions in Chapter 1. 49 | 50 | Installing stuff: 51 | - Download and install Python Anaconda 52 | - $ conda install opencv=3.1 53 | 54 | Cloning the GitHub repo: 55 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 56 | 57 | Running Jupyter notebook: 58 | - $ jupyter notebook 59 | - This will open up a browser window in your current directory. 60 | - Navigate to `opencv-machine-learning/notebooks`. 61 | - Click on the notebook of your choice. 62 | - Select `Kernel > Restart & Run All`. 63 | 64 | Have fun! 65 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter01/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | All you have to do is go to: 36 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 37 | 38 | However, at time of writing this (3/2/2017), MyBinder seems to be down, which 39 | probably has to do with the recent outtage of Amazon AWS: 40 | http://mybinder.org/status 41 | 42 | I hope it will be up again soon. 43 | 44 | 45 | 46 | ### Using Jupyter notebook 47 | 48 | You basically want to follow the installation instructions in Chapter 1. 49 | 50 | Installing stuff: 51 | - Download and install Python Anaconda 52 | - $ conda install opencv=3.1 53 | 54 | Cloning the GitHub repo: 55 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 56 | 57 | Running Jupyter notebook: 58 | - $ jupyter notebook 59 | - This will open up a browser window in your current directory. 60 | - Navigate to `opencv-machine-learning/notebooks`. 61 | - Click on the notebook of your choice. 62 | - Select `Kernel > Restart & Run All`. 63 | 64 | Have fun! 65 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter02/Old/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | All you have to do is go to: 36 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 37 | 38 | However, at time of writing this (3/2/2017), MyBinder seems to be down, which 39 | probably has to do with the recent outtage of Amazon AWS: 40 | http://mybinder.org/status 41 | 42 | I hope it will be up again soon. 43 | 44 | 45 | 46 | ### Using Jupyter notebook 47 | 48 | You basically want to follow the installation instructions in Chapter 1. 49 | 50 | Installing stuff: 51 | - Download and install Python Anaconda 52 | - $ conda install opencv=3.1 53 | 54 | Cloning the GitHub repo: 55 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 56 | 57 | Running Jupyter notebook: 58 | - $ jupyter notebook 59 | - This will open up a browser window in your current directory. 60 | - Navigate to `opencv-machine-learning/notebooks`. 61 | - Click on the notebook of your choice. 62 | - Select `Kernel > Restart & Run All`. 63 | 64 | Have fun! 65 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter02/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | All you have to do is go to: 36 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 37 | 38 | However, at time of writing this (3/2/2017), MyBinder seems to be down, which 39 | probably has to do with the recent outtage of Amazon AWS: 40 | http://mybinder.org/status 41 | 42 | I hope it will be up again soon. 43 | 44 | 45 | 46 | ### Using Jupyter notebook 47 | 48 | You basically want to follow the installation instructions in Chapter 1. 49 | 50 | Installing stuff: 51 | - Download and install Python Anaconda 52 | - $ conda install opencv=3.1 53 | 54 | Cloning the GitHub repo: 55 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 56 | 57 | Running Jupyter notebook: 58 | - $ jupyter notebook 59 | - This will open up a browser window in your current directory. 60 | - Navigate to `opencv-machine-learning/notebooks`. 61 | - Click on the notebook of your choice. 62 | - Select `Kernel > Restart & Run All`. 63 | 64 | Have fun! 65 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter03/New/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | All you have to do is go to: 36 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 37 | 38 | If this website does not show all corresponding notebooks of the chapter, 39 | the build is possibly outdated. In this case, please go to: 40 | http://mybinder.org/status/mbeyeler/opencv-machine-learning 41 | Then rebuild the code, and click launch. Now everything should be up-to-date. 42 | 43 | 44 | 45 | ### Using Jupyter notebook 46 | 47 | You basically want to follow the installation instructions in Chapter 1. 48 | 49 | Installing stuff: 50 | - Download and install Python Anaconda 51 | - $ conda config --add channels conda-forge 52 | - $ conda install opencv=3.1 53 | 54 | Cloning the GitHub repo: 55 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 56 | 57 | Running Jupyter notebook: 58 | - $ jupyter notebook 59 | - This will open up a browser window in your current directory. 60 | - Navigate to `opencv-machine-learning/notebooks`. 61 | - Click on the notebook of your choice. 62 | - Select `Kernel > Restart & Run All`. 63 | 64 | Have fun! 65 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter03/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | All you have to do is go to: 36 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 37 | 38 | If this website does not show all corresponding notebooks of the chapter, 39 | the build is possibly outdated. In this case, please go to: 40 | http://mybinder.org/status/mbeyeler/opencv-machine-learning 41 | Then rebuild the code, and click launch. Now everything should be up-to-date. 42 | 43 | 44 | 45 | ### Using Jupyter notebook 46 | 47 | You basically want to follow the installation instructions in Chapter 1. 48 | 49 | Installing stuff: 50 | - Download and install Python Anaconda 51 | - $ conda config --add channels conda-forge 52 | - $ conda install opencv=3.1 53 | 54 | Cloning the GitHub repo: 55 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 56 | 57 | Running Jupyter notebook: 58 | - $ jupyter notebook 59 | - This will open up a browser window in your current directory. 60 | - Navigate to `opencv-machine-learning/notebooks`. 61 | - Click on the notebook of your choice. 62 | - Select `Kernel > Restart & Run All`. 63 | 64 | Have fun! 65 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter04/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | 36 | First, make sure the website is currently online: 37 | http://mybinder.org/status 38 | 39 | Then go to: 40 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 41 | 42 | This will create a Docker environment that has all necessary packages installed. 43 | Simply execute the code from within the Jupyter notebook. 44 | 45 | If the website does not show all corresponding notebooks of the chapter, 46 | the build is possibly outdated. In this case, please go to: 47 | http://mybinder.org/status/mbeyeler/opencv-machine-learning 48 | Then click on rebuild the code, then click launch. 49 | Now everything should be up-to-date. 50 | 51 | 52 | 53 | ### Using Jupyter notebook 54 | 55 | You basically want to follow the installation instructions in Chapter 1. 56 | 57 | Installing stuff: 58 | - Download and install Python Anaconda 59 | - $ conda config --add channels conda-forge 60 | - $ conda install opencv=3.1 61 | 62 | Cloning the GitHub repo: 63 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 64 | 65 | Running Jupyter notebook: 66 | - $ jupyter notebook 67 | - This will open up a browser window in your current directory. 68 | - Navigate to `opencv-machine-learning/notebooks`. 69 | - Click on the notebook of your choice. 70 | - Select `Kernel > Restart & Run All`. 71 | 72 | Have fun! 73 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter05/05.00-Using-Decision-Trees-to-Make-a-Medical-Diagnosis.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "deletable": true, 7 | "editable": true 8 | }, 9 | "source": [ 10 | "\n", 11 | "\n", 12 | "*This notebook contains an excerpt from the upcoming book [Machine Learning for OpenCV](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv) by Michael Beyeler (expected Aug 2017).\n", 13 | "The code is released under the [MIT license](https://opensource.org/licenses/MIT),\n", 14 | "and is available on [GitHub](https://github.com/mbeyeler/opencv-machine-learning).*\n", 15 | "\n", 16 | "*Note that this excerpt contains only the raw code - the book is rich with additional explanations and illustrations.\n", 17 | "If you find this content useful, please consider supporting the work by\n", 18 | "[buying the book](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv)!*" 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": { 24 | "deletable": true, 25 | "editable": true 26 | }, 27 | "source": [ 28 | "\n", 29 | "< [Representing images](04.05-Representing-Images.ipynb) | [Contents](../README.md) | [Building your first decision tree](05.01-Building-Your-First-Decision-Tree.ipynb) >" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "# Using Decision Trees to Make a Medical Diagnosis\n", 37 | "\n", 38 | "## Understanding Decision Trees\n", 39 | "\n", 40 | "Decision trees are simple yet powerful model for supervised learning problems. Like the name suggests, you can think of them as a tree in which information flows along different branches in the tree - starting at the trunk, and going all the way to the individual leaves.\n", 41 | "\n", 42 | "Let's say we have a dataset consisting of a single e-mail:" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 1, 48 | "metadata": { 49 | "collapsed": false, 50 | "deletable": true, 51 | "editable": true 52 | }, 53 | "outputs": [], 54 | "source": [ 55 | "data = [\n", 56 | " 'I am Mohammed Abacha, the son of the late Nigerian Head of '\n", 57 | " 'State who died on the 8th of June 1998. Since i have been '\n", 58 | " 'unsuccessful in locating the relatives for over 2 years now '\n", 59 | " 'I seek your consent to present you as the next of kin so '\n", 60 | " 'that the proceeds of this account valued at US$15.5 Million '\n", 61 | " 'Dollars can be paid to you. If you are capable and willing '\n", 62 | " 'to assist, contact me at once via email with following '\n", 63 | " 'details: 1. Your full name, address, and telephone number. '\n", 64 | " '2. Your Bank Name, Address. 3.Your Bank Account Number and '\n", 65 | " 'Beneficiary Name - You must be the signatory.'\n", 66 | "]" 67 | ] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "metadata": {}, 72 | "source": [ 73 | "This data can be vectorized using Scikit-Learn's `CountVectorizer`, which turns the e-mail into its individual words and corresponding word counts:" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": 2, 79 | "metadata": { 80 | "collapsed": false, 81 | "deletable": true, 82 | "editable": true 83 | }, 84 | "outputs": [], 85 | "source": [ 86 | "from sklearn.feature_extraction.text import CountVectorizer\n", 87 | "vec = CountVectorizer()\n", 88 | "X = vec.fit_transform(data)" 89 | ] 90 | }, 91 | { 92 | "cell_type": "markdown", 93 | "metadata": {}, 94 | "source": [ 95 | "Let's look at the first five words in the dictionaries, and their word counts:" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": 3, 101 | "metadata": { 102 | "collapsed": false, 103 | "deletable": true, 104 | "editable": true 105 | }, 106 | "outputs": [ 107 | { 108 | "data": { 109 | "text/plain": [ 110 | "['15', '1998', '8th', 'abacha', 'account']" 111 | ] 112 | }, 113 | "execution_count": 3, 114 | "metadata": {}, 115 | "output_type": "execute_result" 116 | } 117 | ], 118 | "source": [ 119 | "vec.get_feature_names()[:5]" 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 4, 125 | "metadata": { 126 | "collapsed": false, 127 | "deletable": true, 128 | "editable": true 129 | }, 130 | "outputs": [ 131 | { 132 | "data": { 133 | "text/plain": [ 134 | "array([1, 1, 1, 1, 2], dtype=int64)" 135 | ] 136 | }, 137 | "execution_count": 4, 138 | "metadata": {}, 139 | "output_type": "execute_result" 140 | } 141 | ], 142 | "source": [ 143 | "X.toarray()[0, :5]" 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "metadata": {}, 149 | "source": [ 150 | "So, how would you have checked if the e-mail is from a Nigerian prince?\n", 151 | "\n", 152 | "One way to do this is to look if the e-mail contained both the words 'nigerian' and 'prince':" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 5, 158 | "metadata": { 159 | "collapsed": false, 160 | "deletable": true, 161 | "editable": true 162 | }, 163 | "outputs": [ 164 | { 165 | "data": { 166 | "text/plain": [ 167 | "True" 168 | ] 169 | }, 170 | "execution_count": 5, 171 | "metadata": {}, 172 | "output_type": "execute_result" 173 | } 174 | ], 175 | "source": [ 176 | "'nigerian' in vec.get_feature_names()" 177 | ] 178 | }, 179 | { 180 | "cell_type": "code", 181 | "execution_count": 6, 182 | "metadata": { 183 | "collapsed": false, 184 | "deletable": true, 185 | "editable": true 186 | }, 187 | "outputs": [ 188 | { 189 | "data": { 190 | "text/plain": [ 191 | "False" 192 | ] 193 | }, 194 | "execution_count": 6, 195 | "metadata": {}, 196 | "output_type": "execute_result" 197 | } 198 | ], 199 | "source": [ 200 | "'prince' in vec.get_feature_names()" 201 | ] 202 | }, 203 | { 204 | "cell_type": "markdown", 205 | "metadata": { 206 | "collapsed": true, 207 | "deletable": true, 208 | "editable": true 209 | }, 210 | "source": [ 211 | "And what do we find to our surprise? The word 'prince' does not occur in the e-mail.\n", 212 | "\n", 213 | "Does this mean the message is legit?\n", 214 | "\n", 215 | "No, of course not. Instead of 'prince', the e-mail went with the words 'head of state' instead - effectively circumventing our all-too-simple spam detector.\n", 216 | "\n", 217 | "Luckily, the theoretical framework behind decision trees provides us with help with both finding the right decision rules as well as which decisions to tackle next." 218 | ] 219 | }, 220 | { 221 | "cell_type": "markdown", 222 | "metadata": { 223 | "deletable": true, 224 | "editable": true 225 | }, 226 | "source": [ 227 | "\n", 228 | "< [Representing images](04.05-Representing-Images.ipynb) | [Contents](../README.md) | [Building your first decision tree](05.01-Building-Your-First-Decision-Tree.ipynb) >" 229 | ] 230 | } 231 | ], 232 | "metadata": { 233 | "anaconda-cloud": {}, 234 | "kernelspec": { 235 | "display_name": "Python 3", 236 | "language": "python", 237 | "name": "python3" 238 | }, 239 | "language_info": { 240 | "codemirror_mode": { 241 | "name": "ipython", 242 | "version": 3 243 | }, 244 | "file_extension": ".py", 245 | "mimetype": "text/x-python", 246 | "name": "python", 247 | "nbconvert_exporter": "python", 248 | "pygments_lexer": "ipython3", 249 | "version": "3.5.2" 250 | } 251 | }, 252 | "nbformat": 4, 253 | "nbformat_minor": 0 254 | } 255 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter05/05.02-Using-Decision-Trees-to-Diagnose-Breast-Cancer.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "\n", 8 | "\n", 9 | "*This notebook contains an excerpt from the upcoming book [Machine Learning for OpenCV](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv) by Michael Beyeler (expected Aug 2017).\n", 10 | "The code is released under the [MIT license](https://opensource.org/licenses/MIT),\n", 11 | "and is available on [GitHub](https://github.com/mbeyeler/opencv-machine-learning).*\n", 12 | "\n", 13 | "*Note that this excerpt contains only the raw code - the book is rich with additional explanations and illustrations.\n", 14 | "If you find this content useful, please consider supporting the work by\n", 15 | "[buying the book](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv)!*" 16 | ] 17 | }, 18 | { 19 | "cell_type": "markdown", 20 | "metadata": {}, 21 | "source": [ 22 | "\n", 23 | "< [Building your first decision tree](05.01-Building-Your-First-Decision-Tree.ipynb) | [Contents](../README.md) | [Using Decision Trees for Regression](05.03-Using-Decision-Trees-for-Regression.ipynb) >" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "# Using Decision Trees to Diagnose Breast Cancer\n", 31 | "\n", 32 | "Now that we have built our first decision trees, it's time to turn our attention to a real dataset: The Breast Cancer Wisconsin dataset .\n", 33 | "\n", 34 | "In order to make the take feasible, the researchers performed feature extraction on the images, like we did in Chapter 4, Representing Data and Engineering Features. They went through a total of 569 images, and extracted 30 different features that describe the characteristics of the cell nuclei present in the images, including:\n", 35 | "\n", 36 | "- cell nucleus texture (represented by the standard deviation of the gray-scale values)\n", 37 | "\n", 38 | "- cell nucleus size (calculated as the mean of distances from center to points on the perimeter)\n", 39 | "\n", 40 | "- tissue smoothness (local variation in radius lengths)\n", 41 | "\n", 42 | "- tissue compactness\n", 43 | "\n", 44 | "The goal of the research was then to classify tissue samples into benign and malignant (a binary classification task)." 45 | ] 46 | }, 47 | { 48 | "cell_type": "markdown", 49 | "metadata": {}, 50 | "source": [ 51 | "## Loading the dataset\n", 52 | "\n", 53 | "The full dataset is part of Scikit-Learn's example datasets:" 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": 1, 59 | "metadata": { 60 | "collapsed": true, 61 | "deletable": true, 62 | "editable": true 63 | }, 64 | "outputs": [], 65 | "source": [ 66 | "from sklearn import datasets\n", 67 | "data = datasets.load_breast_cancer()" 68 | ] 69 | }, 70 | { 71 | "cell_type": "markdown", 72 | "metadata": {}, 73 | "source": [ 74 | "As in previous examples, all data is contained in a 2-D feature matrix data.data, where the rows represent data samples, and the columns are the feature values:" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": 2, 80 | "metadata": { 81 | "collapsed": false, 82 | "deletable": true, 83 | "editable": true 84 | }, 85 | "outputs": [ 86 | { 87 | "data": { 88 | "text/plain": [ 89 | "(569, 30)" 90 | ] 91 | }, 92 | "execution_count": 2, 93 | "metadata": {}, 94 | "output_type": "execute_result" 95 | } 96 | ], 97 | "source": [ 98 | "data.data.shape" 99 | ] 100 | }, 101 | { 102 | "cell_type": "markdown", 103 | "metadata": {}, 104 | "source": [ 105 | "With a look at the provided feature names, we recognize some that we mentioned above:" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 3, 111 | "metadata": { 112 | "collapsed": false, 113 | "deletable": true, 114 | "editable": true 115 | }, 116 | "outputs": [ 117 | { 118 | "data": { 119 | "text/plain": [ 120 | "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n", 121 | " 'mean smoothness', 'mean compactness', 'mean concavity',\n", 122 | " 'mean concave points', 'mean symmetry', 'mean fractal dimension',\n", 123 | " 'radius error', 'texture error', 'perimeter error', 'area error',\n", 124 | " 'smoothness error', 'compactness error', 'concavity error',\n", 125 | " 'concave points error', 'symmetry error', 'fractal dimension error',\n", 126 | " 'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n", 127 | " 'worst smoothness', 'worst compactness', 'worst concavity',\n", 128 | " 'worst concave points', 'worst symmetry', 'worst fractal dimension'], \n", 129 | " dtype='" 427 | ] 428 | }, 429 | "execution_count": 15, 430 | "metadata": {}, 431 | "output_type": "execute_result" 432 | }, 433 | { 434 | "data": { 435 | "image/png": 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ent6p48IiuLlcrlZfRHl5uVwul5qbm1VeXt6mHQC+ZZnNUkN9q0uKqq+X1c4lRdXX+Uaz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\nAAAASUVORK5CYII=\n", 436 | "text/plain": [ 437 | "" 438 | ] 439 | }, 440 | "metadata": {}, 441 | "output_type": "display_data" 442 | } 443 | ], 444 | "source": [ 445 | "plt.figure(figsize=(10, 6))\n", 446 | "plt.plot(max_depths, train_score, 'o-', linewidth=3, label='train')\n", 447 | "plt.plot(max_depths, test_score, 's-', linewidth=3, label='test')\n", 448 | "plt.xlabel('max_depth')\n", 449 | "plt.ylabel('score')\n", 450 | "plt.ylim(0.85, 1.1)\n", 451 | "plt.legend()" 452 | ] 453 | }, 454 | { 455 | "cell_type": "markdown", 456 | "metadata": {}, 457 | "source": [ 458 | "Let's do one more. What about the minimum numbers of samples required to make a node a leaf node?\n", 459 | "\n", 460 | "We repeat the procedure from above:" 461 | ] 462 | }, 463 | { 464 | "cell_type": "code", 465 | "execution_count": 16, 466 | "metadata": { 467 | "collapsed": false, 468 | "deletable": true, 469 | "editable": true 470 | }, 471 | "outputs": [], 472 | "source": [ 473 | "train_score = []\n", 474 | "test_score = []\n", 475 | "min_samples = np.array([2, 4, 8, 16, 32])\n", 476 | "for s in min_samples:\n", 477 | " dtc = tree.DecisionTreeClassifier(min_samples_leaf=s, random_state=42)\n", 478 | " dtc.fit(X_train, y_train)\n", 479 | " train_score.append(dtc.score(X_train, y_train))\n", 480 | " test_score.append(dtc.score(X_test, y_test))" 481 | ] 482 | }, 483 | { 484 | "cell_type": "markdown", 485 | "metadata": {}, 486 | "source": [ 487 | "This leads to a plot that looks quite different from the one before:" 488 | ] 489 | }, 490 | { 491 | "cell_type": "code", 492 | "execution_count": 17, 493 | "metadata": { 494 | "collapsed": false, 495 | "deletable": true, 496 | "editable": true 497 | }, 498 | "outputs": [ 499 | { 500 | "data": { 501 | "text/plain": [ 502 | "" 503 | ] 504 | }, 505 | "execution_count": 17, 506 | "metadata": {}, 507 | "output_type": "execute_result" 508 | }, 509 | { 510 | "data": { 511 | "image/png": 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Xl0dRURGlpaVYLBbCw8P54x//yIkTJxg0aBCzZs0iODi40+dZvXo1q1evBuDl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\nP91GtAAAAABJRU5ErkJggg==\n", 512 | "text/plain": [ 513 | "" 514 | ] 515 | }, 516 | "metadata": {}, 517 | "output_type": "display_data" 518 | } 519 | ], 520 | "source": [ 521 | "plt.figure(figsize=(10, 6))\n", 522 | "plt.plot(min_samples, train_score, 'o-', linewidth=3, label='train')\n", 523 | "plt.plot(min_samples, test_score, 's-', linewidth=3, label='test')\n", 524 | "plt.xlabel('min_samples_leaf')\n", 525 | "plt.ylabel('score')\n", 526 | "plt.ylim(0.9, 1)\n", 527 | "plt.legend()" 528 | ] 529 | }, 530 | { 531 | "cell_type": "markdown", 532 | "metadata": { 533 | "deletable": true, 534 | "editable": true 535 | }, 536 | "source": [ 537 | "What does this all mean? Refer to Chapter 5 for the answers." 538 | ] 539 | }, 540 | { 541 | "cell_type": "markdown", 542 | "metadata": {}, 543 | "source": [ 544 | "\n", 545 | "< [Building your first decision tree](05.01-Building-Your-First-Decision-Tree.ipynb) | [Contents](../README.md) | [Using Decision Trees for Regression](05.03-Using-Decision-Trees-for-Regression.ipynb) >" 546 | ] 547 | } 548 | ], 549 | "metadata": { 550 | "anaconda-cloud": {}, 551 | "kernelspec": { 552 | "display_name": "Python 3", 553 | "language": "python", 554 | "name": "python3" 555 | }, 556 | "language_info": { 557 | "codemirror_mode": { 558 | "name": "ipython", 559 | "version": 3 560 | }, 561 | "file_extension": ".py", 562 | "mimetype": "text/x-python", 563 | "name": "python", 564 | "nbconvert_exporter": "python", 565 | "pygments_lexer": "ipython3", 566 | "version": "3.5.2" 567 | } 568 | }, 569 | "nbformat": 4, 570 | "nbformat_minor": 2 571 | } 572 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter05/05.03-Using-Decision-Trees-for-Regression.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "deletable": true, 7 | "editable": true 8 | }, 9 | "source": [ 10 | "\n", 11 | "\n", 12 | "*This notebook contains an excerpt from the upcoming book [Machine Learning for OpenCV](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv) by Michael Beyeler (expected Aug 2017).\n", 13 | "The code is released under the [MIT license](https://opensource.org/licenses/MIT),\n", 14 | "and is available on [GitHub](https://github.com/mbeyeler/opencv-machine-learning).*\n", 15 | "\n", 16 | "*Note that this excerpt contains only the raw code - the book is rich with additional explanations and illustrations.\n", 17 | "If you find this content useful, please consider supporting the work by\n", 18 | "[buying the book](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv)!*" 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": { 24 | "deletable": true, 25 | "editable": true 26 | }, 27 | "source": [ 28 | "\n", 29 | "< [Using Decision Trees to Diagnose Breast Cancer](05.02-Using-Decision-Trees-to-Diagnose-Breast-Cancer.ipynb) | [Contents](../README.md) | [Detecting Pedestrians with Support Vector Machines](06.00-Detecting-Pedestrians-with-Support-Vector-Machines.ipynb) >" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "# Using Decision Trees for Regression\n", 37 | "\n", 38 | "Although we have so far focused on using decision trees in classification tasks, you can also use them for regression. But, you will need to use Scikit-Learn again, as OpenCV does not provide this flexibility. We therefore only briefly review its functionality here.\n", 39 | "\n", 40 | "Let's say we wanted to use a decision tree to fit a sine wave. To make things interesting, we will also add some noise to the data points using NumPy's random number generator:" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 1, 46 | "metadata": { 47 | "collapsed": true, 48 | "deletable": true, 49 | "editable": true 50 | }, 51 | "outputs": [], 52 | "source": [ 53 | "import numpy as np\n", 54 | "rng = np.random.RandomState(42)" 55 | ] 56 | }, 57 | { 58 | "cell_type": "markdown", 59 | "metadata": {}, 60 | "source": [ 61 | "We then create 100 x values between 0 and 5, and calculate the corresponding sine values:" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 2, 67 | "metadata": { 68 | "collapsed": false, 69 | "deletable": true, 70 | "editable": true 71 | }, 72 | "outputs": [], 73 | "source": [ 74 | "X = np.sort(5 * rng.rand(100, 1), axis=0)\n", 75 | "y = np.sin(X).ravel()" 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": {}, 81 | "source": [ 82 | "We then add noise to every other data point in y (using y[::2]), scaled by 0.5 so we don't introduce to much jitter:" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 3, 88 | "metadata": { 89 | "collapsed": true, 90 | "deletable": true, 91 | "editable": true 92 | }, 93 | "outputs": [], 94 | "source": [ 95 | "y[::2] += 0.5 * (0.5 - rng.rand(50))" 96 | ] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": {}, 101 | "source": [ 102 | "You can then create regression tree like any other tree before. We will build two trees, one with a depth of 2, and one with a depth of 5:" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 4, 108 | "metadata": { 109 | "collapsed": true, 110 | "deletable": true, 111 | "editable": true 112 | }, 113 | "outputs": [], 114 | "source": [ 115 | "from sklearn import tree" 116 | ] 117 | }, 118 | { 119 | "cell_type": "code", 120 | "execution_count": 5, 121 | "metadata": { 122 | "collapsed": false, 123 | "deletable": true, 124 | "editable": true 125 | }, 126 | "outputs": [ 127 | { 128 | "data": { 129 | "text/plain": [ 130 | "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n", 131 | " max_leaf_nodes=None, min_impurity_split=1e-07,\n", 132 | " min_samples_leaf=1, min_samples_split=2,\n", 133 | " min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n", 134 | " splitter='best')" 135 | ] 136 | }, 137 | "execution_count": 5, 138 | "metadata": {}, 139 | "output_type": "execute_result" 140 | } 141 | ], 142 | "source": [ 143 | "regr1 = tree.DecisionTreeRegressor(max_depth=2, random_state=42)\n", 144 | "regr1.fit(X, y)" 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": 6, 150 | "metadata": { 151 | "collapsed": false, 152 | "deletable": true, 153 | "editable": true 154 | }, 155 | "outputs": [ 156 | { 157 | "data": { 158 | "text/plain": [ 159 | "DecisionTreeRegressor(criterion='mse', max_depth=5, max_features=None,\n", 160 | " max_leaf_nodes=None, min_impurity_split=1e-07,\n", 161 | " min_samples_leaf=1, min_samples_split=2,\n", 162 | " min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n", 163 | " splitter='best')" 164 | ] 165 | }, 166 | "execution_count": 6, 167 | "metadata": {}, 168 | "output_type": "execute_result" 169 | } 170 | ], 171 | "source": [ 172 | "regr2 = tree.DecisionTreeRegressor(max_depth=5, random_state=42)\n", 173 | "regr2.fit(X, y)" 174 | ] 175 | }, 176 | { 177 | "cell_type": "markdown", 178 | "metadata": {}, 179 | "source": [ 180 | "We can then use the decision tree like a linear regressor from Chapter 3, First Steps in Supervised Learning. For this we create a test set with x values densely sampled in the whole range from 0 through 5:" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 7, 186 | "metadata": { 187 | "collapsed": true, 188 | "deletable": true, 189 | "editable": true 190 | }, 191 | "outputs": [], 192 | "source": [ 193 | "X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]" 194 | ] 195 | }, 196 | { 197 | "cell_type": "markdown", 198 | "metadata": {}, 199 | "source": [ 200 | "The predicted y values can then be obtained with the predict method:" 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": 8, 206 | "metadata": { 207 | "collapsed": true, 208 | "deletable": true, 209 | "editable": true 210 | }, 211 | "outputs": [], 212 | "source": [ 213 | "y_1 = regr1.predict(X_test)\n", 214 | "y_2 = regr2.predict(X_test)" 215 | ] 216 | }, 217 | { 218 | "cell_type": "markdown", 219 | "metadata": {}, 220 | "source": [ 221 | "If we plot all of these together, we can see how the decision trees differ:" 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "execution_count": 9, 227 | "metadata": { 228 | "collapsed": false, 229 | "deletable": true, 230 | "editable": true 231 | }, 232 | "outputs": [ 233 | { 234 | "data": { 235 | "text/plain": [ 236 | "" 237 | ] 238 | }, 239 | "execution_count": 9, 240 | "metadata": {}, 241 | "output_type": "execute_result" 242 | }, 243 | { 244 | "data": { 245 | "image/png": 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7ZuBk5b7UEFH8YgBHAXMuPpogun74BFLnLdx61/aaNm0ay4dQwCSn/9POqzHY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\nGBEREREFsuhBrVTghRwdNu/786BVqLHpMxCTMgkA0K3zX8g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246 | "text/plain": [ 247 | "" 248 | ] 249 | }, 250 | "metadata": {}, 251 | "output_type": "display_data" 252 | } 253 | ], 254 | "source": [ 255 | "import matplotlib.pyplot as plt\n", 256 | "%matplotlib inline\n", 257 | "plt.style.use('ggplot')\n", 258 | "\n", 259 | "plt.figure(figsize=(10, 6))\n", 260 | "plt.scatter(X, y, c='k', s=50, label='data')\n", 261 | "plt.plot(X_test, y_1, label=\"max_depth=2\", linewidth=5)\n", 262 | "plt.plot(X_test, y_2, label=\"max_depth=5\", linewidth=3)\n", 263 | "plt.xlabel(\"data\")\n", 264 | "plt.ylabel(\"target\")\n", 265 | "plt.legend()" 266 | ] 267 | }, 268 | { 269 | "cell_type": "markdown", 270 | "metadata": { 271 | "deletable": true, 272 | "editable": true 273 | }, 274 | "source": [ 275 | "\n", 276 | "< [Using Decision Trees to Diagnose Breast Cancer](05.02-Using-Decision-Trees-to-Diagnose-Breast-Cancer.ipynb) | [Contents](../README.md) | [Detecting Pedestrians with Support Vector Machines](06.00-Detecting-Pedestrians-with-Support-Vector-Machines.ipynb) >" 277 | ] 278 | } 279 | ], 280 | "metadata": { 281 | "anaconda-cloud": {}, 282 | "kernelspec": { 283 | "display_name": "Python 3", 284 | "language": "python", 285 | "name": "python3" 286 | }, 287 | "language_info": { 288 | "codemirror_mode": { 289 | "name": "ipython", 290 | "version": 3 291 | }, 292 | "file_extension": ".py", 293 | "mimetype": "text/x-python", 294 | "name": "python", 295 | "nbconvert_exporter": "python", 296 | "pygments_lexer": "ipython3", 297 | "version": "3.5.2" 298 | } 299 | }, 300 | "nbformat": 4, 301 | "nbformat_minor": 2 302 | } 303 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter06/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | 36 | First, make sure the website is currently online: 37 | http://mybinder.org/status 38 | 39 | Then go to: 40 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 41 | 42 | This will create a Docker environment that has all necessary packages installed. 43 | Simply execute the code from within the Jupyter notebook. 44 | 45 | If the website does not show all corresponding notebooks of the chapter, 46 | the build is possibly outdated. In this case, please go to: 47 | http://mybinder.org/status/mbeyeler/opencv-machine-learning 48 | Then click on rebuild the code, then click launch. 49 | Now everything should be up-to-date. 50 | 51 | 52 | 53 | ### Using Jupyter notebook 54 | 55 | You basically want to follow the installation instructions in Chapter 1. 56 | 57 | Installing stuff: 58 | - Download and install Python Anaconda 59 | - $ conda config --add channels conda-forge 60 | - $ conda install opencv=3.1 61 | 62 | Cloning the GitHub repo: 63 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 64 | 65 | Running Jupyter notebook: 66 | - $ jupyter notebook 67 | - This will open up a browser window in your current directory. 68 | - Navigate to `opencv-machine-learning/notebooks`. 69 | - Click on the notebook of your choice. 70 | - Select `Kernel > Restart & Run All`. 71 | 72 | Have fun! 73 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter07/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | 36 | First, make sure the website is currently online: 37 | http://mybinder.org/status 38 | 39 | Then go to: 40 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 41 | 42 | This will create a Docker environment that has all necessary packages installed. 43 | Simply execute the code from within the Jupyter notebook. 44 | 45 | If the website does not show all corresponding notebooks of the chapter, 46 | the build is possibly outdated. In this case, please go to: 47 | http://mybinder.org/status/mbeyeler/opencv-machine-learning 48 | Then click on rebuild the code, then click launch. 49 | Now everything should be up-to-date. 50 | 51 | 52 | 53 | ### Using Jupyter notebook 54 | 55 | You basically want to follow the installation instructions in Chapter 1. 56 | 57 | Installing stuff: 58 | - Download and install Python Anaconda 59 | - $ conda config --add channels conda-forge 60 | - $ conda install opencv=3.1 61 | 62 | Cloning the GitHub repo: 63 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 64 | 65 | Running Jupyter notebook: 66 | - $ jupyter notebook 67 | - This will open up a browser window in your current directory. 68 | - Navigate to `opencv-machine-learning/notebooks`. 69 | - Click on the notebook of your choice. 70 | - Select `Kernel > Restart & Run All`. 71 | 72 | Have fun! 73 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter08/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | 36 | First, make sure the website is currently online: 37 | http://mybinder.org/status 38 | 39 | Then go to: 40 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 41 | 42 | This will create a Docker environment that has all necessary packages installed. 43 | Simply execute the code from within the Jupyter notebook. 44 | 45 | If the website does not show all corresponding notebooks of the chapter, 46 | the build is possibly outdated. In this case, please go to: 47 | http://mybinder.org/status/mbeyeler/opencv-machine-learning 48 | Then click on rebuild the code, then click launch. 49 | Now everything should be up-to-date. 50 | 51 | 52 | 53 | ### Using Jupyter notebook 54 | 55 | You basically want to follow the installation instructions in Chapter 1. 56 | 57 | Installing stuff: 58 | - Download and install Python Anaconda 59 | - $ conda config --add channels conda-forge 60 | - $ conda install opencv=3.1 61 | 62 | Cloning the GitHub repo: 63 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 64 | 65 | Running Jupyter notebook: 66 | - $ jupyter notebook 67 | - This will open up a browser window in your current directory. 68 | - Navigate to `opencv-machine-learning/notebooks`. 69 | - Click on the notebook of your choice. 70 | - Select `Kernel > Restart & Run All`. 71 | 72 | Have fun! 73 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter09/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | 36 | First, make sure the website is currently online: 37 | http://mybinder.org/status 38 | 39 | Then go to: 40 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 41 | 42 | This will create a Docker environment that has all necessary packages installed. 43 | Simply execute the code from within the Jupyter notebook. 44 | 45 | If the website does not show all corresponding notebooks of the chapter, 46 | the build is possibly outdated. In this case, please go to: 47 | http://mybinder.org/status/mbeyeler/opencv-machine-learning 48 | Then click on rebuild the code, then click launch. 49 | Now everything should be up-to-date. 50 | 51 | 52 | 53 | ### Using Jupyter notebook 54 | 55 | You basically want to follow the installation instructions in Chapter 1. 56 | 57 | Installing stuff: 58 | - Download and install Python Anaconda 59 | - $ conda config --add channels conda-forge 60 | - $ conda install opencv=3.1 61 | 62 | Cloning the GitHub repo: 63 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 64 | 65 | Running Jupyter notebook: 66 | - $ jupyter notebook 67 | - This will open up a browser window in your current directory. 68 | - Navigate to `opencv-machine-learning/notebooks`. 69 | - Click on the notebook of your choice. 70 | - Select `Kernel > Restart & Run All`. 71 | 72 | Have fun! 73 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter10/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | 36 | First, make sure the website is currently online: 37 | http://mybinder.org/status 38 | 39 | Then go to: 40 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 41 | 42 | This will create a Docker environment that has all necessary packages installed. 43 | Simply execute the code from within the Jupyter notebook. 44 | 45 | If the website does not show all corresponding notebooks of the chapter, 46 | the build is possibly outdated. In this case, please go to: 47 | http://mybinder.org/status/mbeyeler/opencv-machine-learning 48 | Then click on rebuild the code, then click launch. 49 | Now everything should be up-to-date. 50 | 51 | 52 | 53 | ### Using Jupyter notebook 54 | 55 | You basically want to follow the installation instructions in Chapter 1. 56 | 57 | Installing stuff: 58 | - Download and install Python Anaconda 59 | - $ conda config --add channels conda-forge 60 | - $ conda install opencv=3.1 61 | 62 | Cloning the GitHub repo: 63 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 64 | 65 | Running Jupyter notebook: 66 | - $ jupyter notebook 67 | - This will open up a browser window in your current directory. 68 | - Navigate to `opencv-machine-learning/notebooks`. 69 | - Click on the notebook of your choice. 70 | - Select `Kernel > Restart & Run All`. 71 | 72 | Have fun! 73 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter11/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | 36 | First, make sure the website is currently online: 37 | http://mybinder.org/status 38 | 39 | Then go to: 40 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 41 | 42 | This will create a Docker environment that has all necessary packages installed. 43 | Simply execute the code from within the Jupyter notebook. 44 | 45 | If the website does not show all corresponding notebooks of the chapter, 46 | the build is possibly outdated. In this case, please go to: 47 | http://mybinder.org/status/mbeyeler/opencv-machine-learning 48 | Then click on rebuild the code, then click launch. 49 | Now everything should be up-to-date. 50 | 51 | 52 | 53 | ### Using Jupyter notebook 54 | 55 | You basically want to follow the installation instructions in Chapter 1. 56 | 57 | Installing stuff: 58 | - Download and install Python Anaconda 59 | - $ conda config --add channels conda-forge 60 | - $ conda install opencv=3.1 61 | 62 | Cloning the GitHub repo: 63 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 64 | 65 | Running Jupyter notebook: 66 | - $ jupyter notebook 67 | - This will open up a browser window in your current directory. 68 | - Navigate to `opencv-machine-learning/notebooks`. 69 | - Click on the notebook of your choice. 70 | - Select `Kernel > Restart & Run All`. 71 | 72 | Have fun! 73 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Chapter12/code-reviewer-instructions.md: -------------------------------------------------------------------------------- 1 | Reviewer instructions 2 | --------------------- 3 | 4 | All code for this book is maintained in Jupyter notebooks on GitHub: 5 | https://github.com/mbeyeler/opencv-machine-learning 6 | 7 | 8 | ## File organization 9 | 10 | All notebooks can be found here: 11 | https://github.com/mbeyeler/opencv-machine-learning/tree/master/notebooks 12 | 13 | They are organized by chapter and subsection. 14 | For example, the notebooks for Chapter 2 are: 15 | - 02.00-Working-with-Data-in-OpenCV.ipynb 16 | - 02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 17 | - 02.02-Loading-External-Datasets-in-Python.ipynb 18 | - 02.03-Visualizing-Data-Using-Matplotlib.ipynb 19 | - 02.04-Visualizing-Data-from-an-External-Dataset.ipynb 20 | - 02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C++.ipynb 21 | 22 | 23 | 24 | ## Running the code 25 | 26 | There are are at least two ways to run the code: 27 | - from within a Docker container using MyBinder, 28 | - from within a Jupyter notebook on your local machine. 29 | 30 | 31 | 32 | ### Using MyBinder 33 | 34 | MyBinder allows you to run Jupyter notebooks in an interactive Docker container. 35 | 36 | First, make sure the website is currently online: 37 | http://mybinder.org/status 38 | 39 | Then go to: 40 | http://mybinder.org/repo/mbeyeler/opencv-machine-learning 41 | 42 | This will create a Docker environment that has all necessary packages installed. 43 | Simply execute the code from within the Jupyter notebook. 44 | 45 | If the website does not show all corresponding notebooks of the chapter, 46 | the build is possibly outdated. In this case, please go to: 47 | http://mybinder.org/status/mbeyeler/opencv-machine-learning 48 | Then click on rebuild the code, then click launch. 49 | Now everything should be up-to-date. 50 | 51 | 52 | 53 | ### Using Jupyter notebook 54 | 55 | You basically want to follow the installation instructions in Chapter 1. 56 | 57 | Installing stuff: 58 | - Download and install Python Anaconda 59 | - $ conda config --add channels conda-forge 60 | - $ conda install opencv=3.1 61 | 62 | Cloning the GitHub repo: 63 | - $ git clone https://github.com/mbeyeler/opencv-machine-learning 64 | 65 | Running Jupyter notebook: 66 | - $ jupyter notebook 67 | - This will open up a browser window in your current directory. 68 | - Navigate to `opencv-machine-learning/notebooks`. 69 | - Click on the notebook of your choice. 70 | - Select `Kernel > Restart & Run All`. 71 | 72 | Have fun! 73 | -------------------------------------------------------------------------------- /resources/Machine Learning for OpenCV/Software Hardware list.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zslucky/awesome-AI-books/ebdc03b572456f3f1d657fe9d77c6377c3a0b92b/resources/Machine Learning for OpenCV/Software Hardware list.pdf --------------------------------------------------------------------------------