This repo contains an incremental sequence of notebooks designed to teach deep learning, Apache MXNet (incubating), and the gluon interface. Our goal is to leverage the strengths of Jupyter notebooks to present prose, graphics, equations, and code together in one place. If we're successful, the result will be a resource that could be simultaneously a book, course material, a prop for live tutorials, and a resource for plagiarising (with our blessing) useful code. To our knowledge there's no source out there that teaches either (1) the full breadth of concepts in modern deep learning or (2) interleaves an engaging textbook with runnable code. We'll find out by the end of this venture whether or not that void exists for a good reason.
Another unique aspect of this book is its authorship process. We are developing this resource fully in the public view and are making it available for free in its entirety. While the book has a few primary authors to set the tone and shape the content, we welcome contributions from the community and hope to coauthor chapters and entire sections with experts and community members. Already we've received contributions spanning typo corrections through full working examples.
To clone or contribute, visit Deep Learning - The Straight Dope on Github.
To run these notebooks, a recent version of MXNet is required. The easiest way is to install the nightly build MXNet through pip. E.g.:
$ pip install mxnet --pre --user
More detailed instructions are available here
.. toctree:: :glob: :maxdepth: 1 :caption: Crash course chapter01_crashcourse/preface chapter01_crashcourse/introduction chapter01_crashcourse/ndarray chapter01_crashcourse/linear-algebra chapter01_crashcourse/probability chapter01_crashcourse/autograd
.. toctree:: :glob: :maxdepth: 1 :caption: Introduction to supervised learning chapter02_supervised-learning/linear-regression-scratch chapter02_supervised-learning/linear-regression-gluon chapter02_supervised-learning/perceptron chapter02_supervised-learning/softmax-regression-scratch chapter02_supervised-learning/softmax-regression-gluon chapter02_supervised-learning/regularization-scratch chapter02_supervised-learning/regularization-gluon chapter02_supervised-learning/environment
.. toctree:: :glob: :maxdepth: 1 :caption: Deep neural networks chapter03_deep-neural-networks/mlp-scratch chapter03_deep-neural-networks/mlp-gluon chapter03_deep-neural-networks/mlp-dropout-scratch chapter03_deep-neural-networks/mlp-dropout-gluon chapter03_deep-neural-networks/plumbing chapter03_deep-neural-networks/custom-layer chapter03_deep-neural-networks/serialization
.. toctree:: :glob: :maxdepth: 1 :caption: Convolutional neural networks chapter04_convolutional-neural-networks/cnn-scratch chapter04_convolutional-neural-networks/cnn-gluon chapter04_convolutional-neural-networks/deep-cnns-alexnet chapter04_convolutional-neural-networks/very-deep-nets-vgg chapter04_convolutional-neural-networks/cnn-batch-norm-scratch chapter04_convolutional-neural-networks/cnn-batch-norm-gluon
.. toctree:: :glob: :maxdepth: 1 :caption: Recurrent neural networks chapter05_recurrent-neural-networks/simple-rnn chapter05_recurrent-neural-networks/lstm-scratch chapter05_recurrent-neural-networks/gru-scratch chapter05_recurrent-neural-networks/rnns-gluon
.. toctree:: :glob: :maxdepth: 1 :caption: Optimization chapter06_optimization/optimization-intro chapter06_optimization/gd-sgd-scratch chapter06_optimization/gd-sgd-gluon chapter06_optimization/momentum-scratch chapter06_optimization/momentum-gluon chapter06_optimization/adagrad-scratch chapter06_optimization/adagrad-gluon chapter06_optimization/rmsprop-scratch chapter06_optimization/rmsprop-gluon chapter06_optimization/adadelta-scratch chapter06_optimization/adadelta-gluon chapter06_optimization/adam-scratch chapter06_optimization/adam-gluon
.. toctree:: :glob: :maxdepth: 1 :caption: High-performance and distributed training chapter07_distributed-learning/hybridize chapter07_distributed-learning/multiple-gpus-scratch chapter07_distributed-learning/multiple-gpus-gluon chapter07_distributed-learning/training-with-multiple-machines
.. toctree:: :glob: :maxdepth: 1 :caption: Computer vision chapter08_computer-vision/object-detection chapter08_computer-vision/fine-tuning chapter08_computer-vision/visual-question-answer
.. toctree:: :glob: :maxdepth: 1 :caption: Natural language processing chapter09_natural-language-processing/tree-lstm
.. toctree:: :glob: :maxdepth: 1 :caption: Recommender systems chapter11_recommender-systems/intro-recommender-systems
.. toctree:: :glob: :maxdepth: 1 :caption: Time series chapter12_time-series/lds-scratch chapter12_time-series/issm-scratch
.. toctree:: :glob: :maxdepth: 1 :caption: Generative adversarial networks chapter14_generative-adversarial-networks/gan-intro chapter14_generative-adversarial-networks/dcgan chapter14_generative-adversarial-networks/pixel2pixel
.. toctree:: :glob: :maxdepth: 1 :caption: Variational methods chapter18_variational-methods-and-uncertainty/bayes-by-backprop.ipynb chapter18_variational-methods-and-uncertainty/bayes-by-backprop-gluon.ipynb
.. toctree:: :glob: :maxdepth: 1 :caption: Cheat sheets cheatsheets/kaggle-gluon-kfold.ipynb
.. toctree:: :glob: :maxdepth: 1 :caption: Developer documents docs/*