# Deep Learning - The Straight Dope (*Deprecated* Please see d2l.ai)
## This content has been moved to Dive into the Deep Learning Book freely available at https://d2l.ai/.
## Abstract
This repo contains an
incremental sequence of notebooks designed to teach deep learning, MXNet, 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.
## Implementation with Apache MXNet
Throughout this book,
we rely upon MXNet to teach core concepts, advanced topics, and a full
complement of applications. MXNet is widely used in production environments
owing to its strong reputation for speed. Now with ``gluon``, MXNet's new
imperative interface (alpha), doing research in MXNet is easy.
## Dependencies
To run these notebooks, you'll want to build MXNet from source. Fortunately,
this is easy (especially on Linux) if you follow [these
instructions](http://mxnet.io/get_started/install.html). You'll also want to
[install Jupyter](http://jupyter.readthedocs.io/en/latest/install.html) and use
Python 3 (because it's 2017).
## Slides
The authors (& others) are
increasingly giving talks that are based on the content in this books. Some of
these slide-decks (like the 6-hour KDD 2017) are gigantic so we're collecting
them separately in [this repo](https://github.com/zackchase/mxnet-slides).
Contribute there if you'd like to share tutorials or course material based on
this books.
## Translation
As we write the book, large stable sections are simultaneously being translated into 中文,
available in a [web version](http://zh.gluon.ai/) and via [GitHub source](http://zh.gluon.ai/).
## Table of contents
### Part 1: Deep Learning Fundamentals
* **Chapter 1:** Crash course
* [Preface](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter01_crashcourse/preface.ipynb)
* [Introduction](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter01_crashcourse/introduction.ipynb)
* [Manipulating data with NDArray](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter01_crashcourse/ndarray.ipynb)
* [Linear algebra](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter01_crashcourse/linear-algebra.ipynb)
* [Probability and statistics](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter01_crashcourse/probability.ipynb)
* [Automatic differentiation via ``autograd``](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter01_crashcourse/autograd.ipynb)
* **Chapter 2:** Introduction to supervised learning
* [Linear regression *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/linear-regression-scratch.ipynb)
* [Linear regression *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/linear-regression-gluon.ipynb)
* [Binary classification with logistic regression *(``gluon`` w bespoke loss function)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/logistic-regression-gluon.ipynb)
* [Multiclass logistic regression *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/softmax-regression-scratch.ipynb)
* [Multiclass logistic regression *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/softmax-regression-gluon.ipynb)
* [Overfitting and regularization *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/regularization-scratch.ipynb)
* [Overfitting and regularization *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/regularization-gluon.ipynb)
* [Perceptron and SGD primer](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/perceptron.ipynb)
* [Learning environments](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/environment.ipynb)
* **Chapter 3:** Deep neural networks (DNNs)
* [Multilayer perceptrons *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/mlp-scratch.ipynb)
* [Multilayer perceptrons *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/mlp-gluon.ipynb)
* [Dropout regularization *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/mlp-dropout-scratch.ipynb)
* [Dropout regularization *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/mlp-dropout-gluon.ipynb)
* [Introduction to ``gluon.Block`` and ``gluon.nn.Sequential()``](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/plumbing.ipynb)
* [Writing custom layers with ``gluon.Block``](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/custom-layer.ipynb)
* [Serialization: saving and loading models](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/serialization.ipynb)
* Advanced Data IO
* Debugging your neural networks
* **Chapter 4:** Convolutional neural networks (CNNs)
* [Convolutional neural networks *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter04_convolutional-neural-networks/cnn-scratch.ipynb)
* [Convolutional neural networks *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter04_convolutional-neural-networks/cnn-gluon.ipynb)
* [Introduction to deep CNNs (AlexNet)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter04_convolutional-neural-networks/deep-cnns-alexnet.ipynb)
* [Very deep networks and repeating blocks (VGG network)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter04_convolutional-neural-networks/very-deep-nets-vgg.ipynb)
* [Batch normalization *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter04_convolutional-neural-networks/cnn-batch-norm-scratch.ipynb)
* [Batch normalization *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter04_convolutional-neural-networks/cnn-batch-norm-gluon.ipynb)
* **Chapter 5:** Recurrent neural networks (RNNs)
* [Simple RNNs (from scratch)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter05_recurrent-neural-networks/simple-rnn.ipynb)
* [LSTMS RNNs (from scratch)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter05_recurrent-neural-networks/lstm-scratch.ipynb)
* [GRUs (from
（深度学习框架MXnet）mxnet-the-straight-dope-master.zip
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（深度学习框架MXnet）mxnet-the-straight-dope-master.zip （203个子文件） 
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