└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Content 2 | - [Deep learning](https://github.com/sanchit2843/Study_resources#Deep-learning) 3 | - [General](https://github.com/sanchit2843/Study_resources#General) 4 | - [Computer Vision](https://github.com/sanchit2843/Study_resources#Computer-Vision) 5 | - [Natural Language processing](https://github.com/sanchit2843/Study_resources#Natural-Language-processing) 6 | - [Study resources lists](https://github.com/sanchit2843/Study_resources#Study-resources-lists) 7 | - [Robotics](https://github.com/sanchit2843/Study_resources#Robotics) 8 | - [Datasets](https://github.com/sanchit2843/Study_resources#Dataset) 9 | - [Mathematics](https://github.com/sanchit2843/Study_resources#Mathematics) 10 | - [Reinforcement learning](https://github.com/sanchit2843/Study_resources#Reinforcement-learning) 11 | - [Miscellaneous](https://github.com/sanchit2843/Study_resources#Miscellaneous) 12 | 13 | # Deep learning 14 | 15 | ## General 16 | ||Title|Link| 17 | |---|---|---| 18 | |1.|A Recipe for Training Neural Networks. |[Article](http://karpathy.github.io/2019/04/25/recipe)| 19 | |2.|Deep learning books |[D2L.ai](https://d2l.ai/), [Deep learning book](https://www.deeplearningbook.org/) , [Neural network and deep learning](http://neuralnetworksanddeeplearning.com/index.html), [Deep learning wizard](https://www.deeplearningwizard.com/deep_learning/intro/), [A guide to convolutional neural networks for computer vision](https://ieeexplore.ieee.org/document/8295029/)| 20 | |3.|Deep unsupervised learning. |[Videos](https://www.youtube.com/channel/UCf4SX8kAZM_oGcZjMREsU9w/videos)| 21 | |4.|A Neural Network in 13 lines of Python. |[Article](https://iamtrask.github.io/2015/07/27/python-network-part2/)| 22 | |5.| Tensorflow tutorials |[Github](https://github.com/MorvanZhou/Tensorflow-Tutorial)| 23 | |6.|Neural networks by 3 blue 1 brown(Mathematics of gradient descent and backprop). |[Videos](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)| 24 | |7.|Ten techniques learned from fast ai. |[Article](https://blog.floydhub.com/ten-techniques-from-fast-ai/)| 25 | |8.|Understanding AUC - ROC Curve. |[Article](https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5)| 26 | |9.|Adversarial Examples Aren’t Bugs, They’re Features. |[Article](https://medium.com/syncedreview/adversarial-examples-arent-bugs-they-re-features-c4b743975200)| 27 | |10.|Training Very Deep Networks. |[Paper](http://papers.nips.cc/paper/5850-training-very-deep-networks.pdf)| 28 | |11.|Andrej Karpathy blog. |[Website](http://karpathy.github.io/)| 29 | |12.|Incredible pytorch. |[Github](https://github.com/ritchieng/the-incredible-pytorch)| 30 | |13.|Tensorboard with google colab |[Article](https://www.dlology.com/blog/quick-guide-to-run-tensorboard-in-google-colab/)| 31 | |14.|Survey on deep learning with class imbalance. |[Paper](https://link.springer.com/content/pdf/10.1186%2Fs40537-019-0192-5.pdf)| 32 | |15.|Backpropogation algorithm|[Article](https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/)| 33 | |16.|The Mathematics of Machine Learning|[Article](https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568)| 34 | |17.|Deep elarning wizard|[Book](https://www.deeplearningwizard.com/deep_learning/intro/)| 35 | 36 | 37 | ## Computer Vision 38 | ||Title|Link| 39 | |---|---|---| 40 | |1.|A guide to convolution arithmetic for deep learning. |[Paper](https://arxiv.org/abs/1603.07285)| 41 | |2.|One-shot object detection. |[Article](http://machinethink.net/blog/object-detection/)| 42 | |3.|A Brief Introduction To GANs. |[Article](https://medium.com/sigmoid/a-brief-introduction-to-gans-and-how-to-code-them-2620ee465c30)| 43 | |4.|PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection. |[Article](https://towardsdatascience.com/pvanet-deep-but-lightweight-neural-networks-for-real-time-object-detection-aa9de432512)| 44 | |5.|A list of papers and other resources on Generative Adversarial (Neural) Networks. |[Github](https://github.com/nightrome/really-awesome-gan)| 45 | |6.|Face recognition library. |[Website](https://face-recognition.readthedocs.io/en/latest/face_recognition.html)| 46 | |7.|Cycle Gans and Pix2Pix. |[Article](https://towardsdatascience.com/cyclegans-and-pix2pix-5e6a5f0159c4)| 47 | |8.|Object detection blogs and codes|1. [Article](https://cv-tricks.com/object-detection/faster-r-cnn-yolo-ssd/)
2. [Article](https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-learning-495ef744fab)
3. [Article](http://machinethink.net/blog/object-detection/)|
4. [Article](https://tryolabs.com/blog/2018/01/18/faster-r-cnn-down-the-rabbit-hole-of-modern-object-detection/)| 48 | |9.|Generating Diverse High-Fidelity Images with VQ-VAE-2|[Paper](https://arxiv.org/abs/1906.00446)| 49 | |10.|Semantic segmentation over the years. |[Article](https://meetshah1995.github.io/semanticsegmentation/deeplearning/pytorch/visdom/2017/06/01/semantic-segmentation-over-the-years.html)| 50 | |11.|Accident forecasting dataset and paper. |[paper](https://ankitshah009.github.io/accident_forecasting_traffic_camera)| 51 | |12.|A course on evolution of object detection algorithms. |[youtube](https://www.youtube.com/playlist?list=PL1GQaVhO4f_jLxOokW7CS5kY_J1t1T17S)| 52 | |13.|Deep learning for computer vision(UMich, Justin Johnson) |[videos](http://leccap.engin.umich.edu/leccap/viewer/r/v3jaMO)| 53 | |14.|Tracking Objects as Points |[paper](https://arxiv.org/pdf/2004.01177v1.pdf)| 54 | 55 | ## Natural language processing 56 | ||Title|Link| 57 | |---|---|---| 58 | |1.|Natural language processing course stanford(cs224d). |[Video](https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6)| 59 | |2.|Lisbon machine learning school. |[video](https://www.youtube.com/playlist?list=PLToLj8M4ao-fuRfnzEJCCnvuW2_FeJ73N)| 60 | ## Study resources list 61 | ||Title|Link| 62 | |---|---|---| 63 | |1.|Deep learning drizzle. |[Website](https://deep-learning-drizzle.github.io/)| 64 | |2.|8 Fun Machine Learning Projects for Beginners. |[Article](https://elitedatascience.com/machine-learning-projects-for-beginners)| 65 | |3.|6 Deep Learning Applications a beginner can build in minutes. |[Article](https://www.analyticsvidhya.com/blog/2017/02/6-deep-learning-applications-beginner-python/)| 66 | |4.|24 Ultimate Data Science Projects To Boost Your Knowledge and Skills. |[Article](https://www.analyticsvidhya.com/blog/2018/05/24-ultimate-data-science-projects-to-boost-your-knowledge-and-skills/)| 67 | |5.|The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3). |[Article](https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html)| 68 | |6.|Awesome - Most Cited Deep Learning Papers. |[Github](https://github.com/terryum/awesome-deep-learning-papers)| 69 | |7.|Deep learning courses, papers, researchers etc list. |[Github](https://github.com/ChristosChristofidis/awesome-deep-learning)| 70 | |8.|Jeremy jackson deep learning resources. |[Website](http://www.jeremydjacksonphd.com/category/deep-learning/)| 71 | |9.|Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas. |[Github](https://github.com/NirantK/awesome-project-ideas)| 72 | |10.|Deep Learning Papers Reading Roadmap. |[Github](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap)| 73 | |11.|The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. |[Github](https://github.com/ritchieng/the-incredible-pytorch)| 74 | |12.|The Mathematics of Machine Learning|[Article](https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568)| 75 | 76 | ## Robotics 77 | ||Title|Link| 78 | |---|---|---| 79 | |1.|Deep learning for robotics pieter abbeel. |[Video](https://www.youtube.com/watch?v=SYqV543LWoY)| 80 | |2.|Augmenting SLAM with deep learning|[Article](https://www.therobotreport.com/augmenting-slam-with-deep-learning/)| 81 | |3.|Robotics Coursework|[Github list](https://github.com/mithi/robotics-coursework)| 82 | 83 | ## Dataset 84 | ||Title|Link| 85 | |---|---|---| 86 | |1.|Open Datasets |[Skymind](https://skymind.ai/wiki/open-datasets)| 87 | |2.|Quora answer |[Link](https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public)| 88 | |3.|Medical Data |[Github](https://github.com/beamandrew/medical-data)| 89 | 90 | # Mathematics 91 | ||Title|Link| 92 | |---|---|---| 93 | |1.|Calculus summary for deep learning|[Article](http://wiki.fast.ai/index.php/Calculus_for_Deep_Learning)| 94 | 95 | # Reinforcement learning 96 | ||Title|Link| 97 | |---|---|---| 98 | |1.|The Complete Reinforcement Learning Dictionary. |[Article](https://towardsdatascience.com/the-complete-reinforcement-learning-dictionary-e16230b7d24e)| 99 | |2.|Deep reinforcement learning course. |[Articles](https://simoninithomas.github.io/Deep_reinforcement_learning_Course/#syllabus)| 100 | 101 | # Miscellaneous 102 | ||Title|Link| 103 | |---|---|---| 104 | |1.|An introduction to web scrapping in python. |[Article](https://medium.com/@shrutikalra251/an-introduction-to-web-scraping-using-python-edb0ccca42f?sk=a666980542c947ef6af72c9c6c5094b7)| 105 | |2.|ROS |[Website](http://www.theconstructsim.com/)| 106 | |3.|How can i prepare for research oriented goal. |[Reddit](https://www.reddit.com/r/MachineLearning/comments/9e3bne/dhow_can_i_prepare_for_a_research_oriented_role/)| 107 | |4.|Graphical user interface (GUI) for Google Compute Engine instance. |[Article](https://medium.com/google-cloud/graphical-user-interface-gui-for-google-compute-engine-instance-78fccda09e5c)| 108 | 109 | --------------------------------------------------------------------------------