└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning Resources 2 | A curated list of deep learning resources books, courses, papers, libraries, conferences, sample code, and many more. 3 | 4 | ## Table of Contents 5 | * **[Free Books](#free-books)** 6 | 7 | * **[Courses](#courses)** 8 | 9 | * **[Videos and Lectures](#videos-and-lectures)** 10 | 11 | * **[Papers](#papers)** 12 | 13 | * **[Tutorials](#tutorials)** 14 | 15 | * **[Sample Code](#sample-code)** 16 | 17 | * **[Datasets](#datasets)** 18 | 19 | * **[Conferences](#conferences)** 20 | 21 | * **[Libraries](#libraries)** 22 | 23 | ### Free Books 24 | 1. [Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville](http://www.deeplearningbook.org/) 25 | 2. [Deep Learning by Microsoft Research](http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf) 26 | 3. [Neural Networks and Deep Learning by Michael Nielsen](http://neuralnetworksanddeeplearning.com/) 27 | 4. [Neuraltalk by Andrej Karpathy](https://github.com/karpathy/neuraltalk) 28 | 29 | ### Courses 30 | 1. [Neural Networks for Machine Learning](https://class.coursera.org/neuralnets-2012-001) 31 | 2. [Neural networks class](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) 32 | 3. [Deep Learning Course](http://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start) 33 | 4. [A.I - Berkeley](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/courseware/) 34 | 5. [A.I - MIT](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/) 35 | 6. [Convolutional Neural Networks for Visual Recognition - Stanford](http://vision.stanford.edu/teaching/cs231n/syllabus.html) 36 | 7. [Practical Deep Learning For Coders](http://course.fast.ai/) 37 | 8. [MIT 6.S191 Introduction to Deep Learning](http://introtodeeplearning.com/) 38 | 39 | ### Videos and Lectures 40 | 1. [Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24) by Andrew Ng 41 | 2. [Recent Developments in Deep Learning](https://www.youtube.com/watch?v=vShMxxqtDDs&index=3&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT) by Geoff Hinton 42 | 3. [The Unreasonable Effectiveness of Deep Learning](https://www.youtube.com/watch?v=sc-KbuZqGkI) by Yann LeCun 43 | 4. [Deep Learning of Representations](https://www.youtube.com/watch?v=4xsVFLnHC_0) by Yoshua bengio 44 | 5. [Making Sense of the World with Deep Learning](http://vimeo.com/80821560) by Adam Coates 45 | 6. [How Deep Neural Networks Work](https://www.youtube.com/watch?v=ILsA4nyG7I0) 46 | 7. [MIT 6.S191 Introduction to Deep Learning](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) 47 | 48 | ### Papers 49 | 1. [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) 50 | 2. [Using Very Deep Autoencoders for Content Based Image Retrieval](http://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf) 51 | 3. [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf) 52 | 4. [Neural Networks for Named Entity Recognition](http://nlp.stanford.edu/~socherr/pa4_ner.pdf) 53 | 5. [Training tricks by YB](http://www.iro.umontreal.ca/~bengioy/papers/YB-tricks.pdf) 54 | 55 | ### Tutorials 56 | 1. [How to Implement the Backpropagation Algorithm From Scratch In Python](https://machinelearningmastery.com/implement-backpropagation-algorithm-scratch-python/) 57 | 2. [image classifier using convolutional neural network](http://cv-tricks.com/tensorflow-tutorial/training-convolutional-neural-network-for-image-classification/) 58 | 3. [A Beginner’s Guide to Recurrent Networks and LSTMs](https://deeplearning4j.org/lstm.html) 59 | 4. [Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/) 60 | 5. [Stochastic Gradient Descent (SGD) with Python](https://www.pyimagesearch.com/2016/10/17/stochastic-gradient-descent-sgd-with-python/) 61 | 6. [A Guide to Deep Learning in PyTorch](http://belajar.machinelearning.id/panduan/pytorch/) 62 | 7. [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/) 63 | 8. [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) 64 | 65 | ### Sample Code 66 | 1. [Deep Learning with Python](https://github.com/Apress/deep-learning-w-python) 67 | 2. [Deep Learning with TensorFlow](https://github.com/PacktPublishing/Deep-Learning-with-TensorFlow) 68 | 3. [Fundamentals of Deep Learning](https://github.com/darksigma/Fundamentals-of-Deep-Learning-Book) 69 | 4. [Introduction to Deep Learning Using R](https://github.com/Apress/intro-to-deep-learning-using-r) 70 | 71 | ### Datasets 72 | 1. [CIFAR-10 and CIFAR-100](http://www.cs.toronto.edu/~kriz/cifar.html) 73 | 2. [Google House Numbers](http://ufldl.stanford.edu/housenumbers/) from street view 74 | 3. [IMAGENET](http://www.image-net.org/) 75 | 4. [MNIST](http://yann.lecun.com/exdb/mnist/) Handwritten digits 76 | 5. [Tiny Images](http://groups.csail.mit.edu/vision/TinyImages/) 80 Million tiny images6. 77 | 6. [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) 78 | 79 | ### Conferences 80 | 1. [CVPR - IEEE Conference on Computer Vision and Pattern Recognition](http://cvpr2018.thecvf.com) 81 | 2. [AAMAS - International Joint Conference on Autonomous Agents and Multiagent Systems](http://celweb.vuse.vanderbilt.edu/aamas18/) 82 | 3. [IJCAI - International Joint Conference on Artificial Intelligence](https://www.ijcai-18.org/) 83 | 4. [NIPS - Neural Information Processing Systems](https://nips.cc/Conferences/2018) 84 | 5. [ICLR - International Conference on Learning Representations](https://iclr.cc/) 85 | 86 | ### Libraries 87 | 1. [Tensorflow](https://www.tensorflow.org/) 88 | 21. [Keras - A high-level neural networks API running on top of TensorFlow, CNTK, or Theano](http://keras.io) 89 | 1. [Caffe](http://caffe.berkeleyvision.org/) 90 | 2. [Torch7](http://torch.ch/) 91 | 3. [Theano](http://deeplearning.net/software/theano/) 92 | 32. [MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework](https://github.com/dmlc/mxnet/) 93 | 49. [TensorFlow.js - formerly known as deeplearn.js](https://github.com/tensorflow/tfjs-core) 94 | 95 | ## Contributing 96 | Jika anda ingin berkontribusi dalam github ini, sangat disarankan untuk `Pull Request` namun dengan resource berbahasa indonesia. --------------------------------------------------------------------------------