- Deep learning core concepts.
- Deep learning training.
- Understanding LSTM networks
- The Unreasonable Effectiveness of Recurrent Neural Networks shows a bunch of real life examples
- Deep Learning, NLP, and Representations for an overview on word embeddings and RNNs for NLP
- Understanding LSTM Networks is about LSTMs work specifically, but also informative about RNNs in general
- Calculus on Computational Graphs
More examples:
- apachecn/MachineLearning
- Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow
- lawlite19/DeepLearning_Python
- A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
- Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech
- Content for Udacity's Machine Learning curriculum
- This is the lab repository of my honours degree project on machine learning
- A curated list of awesome Machine Learning frameworks, libraries and software
- Bare bones Python implementations of some of the fundamental Machine Learning models and algorithms
- The "Python Machine Learning" book code repository and info resource
PyTorch 0.4+ is recommended.
- Offical PyTorch tutorials for more tutorials (some of these tutorials are included there)
- PyTorch Basics
- Convolutional Neural Network
- Deep Residual Network
- Recurrent Neural Network
- Bidirectional Recurrent Neural Network
- Language Model (RNN-LM)
- Image Captioning (CNN-RNN)
- Deep Convolutional GAN (DCGAN)
- Variational Auto-Encoder
- Neural Style Transfer
- Generative Adversarial Networks
- Variational Auto-Encoder
- Neural Style Transfer
- Image Captioning (CNN-RNN)
- spro/practical-pytorch
- jcjohnson's PyTorch examples for a more in depth overview (including custom modules and autograd functions)
- chenyuntc/pytorch-book
TensorFlow v2.0 is recommended.
- Hello World (notebook). Very simple example to learn how to print "hello world" using TensorFlow 2.0.
- Basic Operations (notebook). A simple example that cover TensorFlow 2.0 basic operations.
- Linear Regression (notebook). Implement a Linear Regression with TensorFlow 2.0.
- Logistic Regression (notebook). Implement a Logistic Regression with TensorFlow 2.0.
- Simple Neural Network (notebook). Implement a Simple Neural Network with TensorFlow 2.0.
- Create your own layer (notebook). Create your own layer with TensorFlow 2.0.
- Simple Neural Network (notebook). Use TensorFlow 2.0 'layers' and 'model' API to build a simple neural network to classify MNIST digits dataset.
- Simple Neural Network (low-level) (notebook). Raw implementation of a simple neural network to classify MNIST digits dataset.
- Convolutional Neural Network (notebook). Use TensorFlow 2.0 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset.
- Convolutional Neural Network (low-level) (notebook). Raw implementation of a convolutional neural network to classify MNIST digits dataset.
- Auto-Encoder (notebook). Build an auto-encoder to encode an image to a lower dimension and re-construct it.
- DCGAN (Deep Convolutional Generative Adversarial Networks) (notebook). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise.
- Save and Restore a model (notebook). Save and Restore a model with TensorFlow 2.0.
- Build Custom Layers & Modules (notebook). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models.
Julia: Looks like Python, feels like Lisp, runs like Fortran
As ML models began to need the full power of a programming language,getting Python to scale to ML’s heavy computational demands is far harder than you might expect. Python’s semantics also make it fundamentally difficult to provide model-level parallelism or compile models for small devices.
More details:
- Tutorials and information on the Julia language for MIT numerical-computation courses
- An Introduction to Julia for Data Science and Scientific Computing
- Introducing Julia wikibook
- Learn Julia the hard way
- Hands-on Julia
- The-Julia-Express
- Julia by Example
- Learn Julia via Examples
- Programming in Julia (Quantitative Economics)
- Machine Learning, Neural Networks, NLP, Speech Recognition and Voice tools
- Julia implementation of the scikit-learn API
- Mocha.jl - Deep Learning framework for Julia
- Merlin.jl - Deep Learning for Julia
- Knet.jl - Koç University deep learning framework
- Julia package for text analysis
- MXNet Julia Package - flexible and efficient deep learning in Julia
- A Julia wrapper for TensorFlow
- Flux is the ML library that doesn't make you tensor
- A julia wrapper for Keras
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