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https://github.com/datascienceid/deep-learning-resources
A curated list of deep learning resources books, courses, papers, libraries, conferences, sample code, and many more.
https://github.com/datascienceid/deep-learning-resources
List: deep-learning-resources
awesome awesome-list conference data-science dataset deep-learning indonesia lecture machine-learning paper science tutorial
Last synced: about 1 month ago
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A curated list of deep learning resources books, courses, papers, libraries, conferences, sample code, and many more.
- Host: GitHub
- URL: https://github.com/datascienceid/deep-learning-resources
- Owner: datascienceid
- Created: 2018-04-14T03:11:57.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-08-09T05:59:06.000Z (about 3 years ago)
- Last Synced: 2024-05-20T05:33:35.016Z (4 months ago)
- Topics: awesome, awesome-list, conference, data-science, dataset, deep-learning, indonesia, lecture, machine-learning, paper, science, tutorial
- Size: 6.84 KB
- Stars: 109
- Watchers: 9
- Forks: 35
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - deep-learning-resources - A curated list of deep learning resources books, courses, papers, libraries, conferences, sample code, and many more. (Other Lists / PowerShell Lists)
README
# Deep Learning Resources
A curated list of deep learning resources books, courses, papers, libraries, conferences, sample code, and many more.
## Table of Contents
* **[Free Books](#free-books)**
* **[Courses](#courses)**
* **[Videos and Lectures](#videos-and-lectures)**
* **[Papers](#papers)**
* **[Tutorials](#tutorials)**
* **[Sample Code](#sample-code)**
* **[Datasets](#datasets)**
* **[Conferences](#conferences)**
* **[Libraries](#libraries)**
### Free Books
1. [Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville](http://www.deeplearningbook.org/)
2. [Deep Learning by Microsoft Research](http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf)
3. [Neural Networks and Deep Learning by Michael Nielsen](http://neuralnetworksanddeeplearning.com/)
4. [Neuraltalk by Andrej Karpathy](https://github.com/karpathy/neuraltalk)
### Courses
1. [Neural Networks for Machine Learning](https://class.coursera.org/neuralnets-2012-001)
2. [Neural networks class](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)
3. [Deep Learning Course](http://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start)
4. [A.I - Berkeley](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/courseware/)
5. [A.I - MIT](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/)
6. [Convolutional Neural Networks for Visual Recognition - Stanford](http://vision.stanford.edu/teaching/cs231n/syllabus.html)
7. [Practical Deep Learning For Coders](http://course.fast.ai/)
8. [MIT 6.S191 Introduction to Deep Learning](http://introtodeeplearning.com/)
### Videos and Lectures
1. [Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24) by Andrew Ng
2. [Recent Developments in Deep Learning](https://www.youtube.com/watch?v=vShMxxqtDDs&index=3&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT) by Geoff Hinton
3. [The Unreasonable Effectiveness of Deep Learning](https://www.youtube.com/watch?v=sc-KbuZqGkI) by Yann LeCun
4. [Deep Learning of Representations](https://www.youtube.com/watch?v=4xsVFLnHC_0) by Yoshua bengio
5. [Making Sense of the World with Deep Learning](http://vimeo.com/80821560) by Adam Coates
6. [How Deep Neural Networks Work](https://www.youtube.com/watch?v=ILsA4nyG7I0)
7. [MIT 6.S191 Introduction to Deep Learning](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI)
### Papers
1. [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
2. [Using Very Deep Autoencoders for Content Based Image Retrieval](http://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf)
3. [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf)
4. [Neural Networks for Named Entity Recognition](http://nlp.stanford.edu/~socherr/pa4_ner.pdf)
5. [Training tricks by YB](http://www.iro.umontreal.ca/~bengioy/papers/YB-tricks.pdf)
### Tutorials
1. [How to Implement the Backpropagation Algorithm From Scratch In Python](https://machinelearningmastery.com/implement-backpropagation-algorithm-scratch-python/)
2. [image classifier using convolutional neural network](http://cv-tricks.com/tensorflow-tutorial/training-convolutional-neural-network-for-image-classification/)
3. [A Beginner’s Guide to Recurrent Networks and LSTMs](https://deeplearning4j.org/lstm.html)
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/)
5. [Stochastic Gradient Descent (SGD) with Python](https://www.pyimagesearch.com/2016/10/17/stochastic-gradient-descent-sgd-with-python/)
6. [A Guide to Deep Learning in PyTorch](http://belajar.machinelearning.id/panduan/pytorch/)
7. [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/)
8. [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/)
### Sample Code
1. [Deep Learning with Python](https://github.com/Apress/deep-learning-w-python)
2. [Deep Learning with TensorFlow](https://github.com/PacktPublishing/Deep-Learning-with-TensorFlow)
3. [Fundamentals of Deep Learning](https://github.com/darksigma/Fundamentals-of-Deep-Learning-Book)
4. [Introduction to Deep Learning Using R](https://github.com/Apress/intro-to-deep-learning-using-r)
### Datasets
1. [CIFAR-10 and CIFAR-100](http://www.cs.toronto.edu/~kriz/cifar.html)
2. [Google House Numbers](http://ufldl.stanford.edu/housenumbers/) from street view
3. [IMAGENET](http://www.image-net.org/)
4. [MNIST](http://yann.lecun.com/exdb/mnist/) Handwritten digits
5. [Tiny Images](http://groups.csail.mit.edu/vision/TinyImages/) 80 Million tiny images6.
6. [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist)
### Conferences
1. [CVPR - IEEE Conference on Computer Vision and Pattern Recognition](http://cvpr2018.thecvf.com)
2. [AAMAS - International Joint Conference on Autonomous Agents and Multiagent Systems](http://celweb.vuse.vanderbilt.edu/aamas18/)
3. [IJCAI - International Joint Conference on Artificial Intelligence](https://www.ijcai-18.org/)
4. [NIPS - Neural Information Processing Systems](https://nips.cc/Conferences/2018)
5. [ICLR - International Conference on Learning Representations](https://iclr.cc/)
### Libraries
1. [Tensorflow](https://www.tensorflow.org/)
21. [Keras - A high-level neural networks API running on top of TensorFlow, CNTK, or Theano](http://keras.io)
1. [Caffe](http://caffe.berkeleyvision.org/)
2. [Torch7](http://torch.ch/)
3. [Theano](http://deeplearning.net/software/theano/)
32. [MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework](https://github.com/dmlc/mxnet/)
49. [TensorFlow.js - formerly known as deeplearn.js](https://github.com/tensorflow/tfjs-core)
## Contributing
Jika anda ingin berkontribusi dalam github ini, sangat disarankan untuk `Pull Request` namun dengan resource berbahasa indonesia.