The Straight Dope

0.1

Crash course

Preface
Introduction
Manipulate data the MXNet way with ndarray
Linear algebra
Intermediate linear algebra
Probability and statistics
Automatic differentiation with autograd

Introduction to supervised learning

Linear regression from scratch
Linear regression with gluon
Binary classification with logistic regression
Multiclass logistic regression from scratch
Multiclass logistic regression with gluon
Overfitting and regularization
Overfitting and regularization (with gluon)
The Perceptron
Environment

Deep neural networks

Multilayer perceptrons from scratch
Multilayer perceptrons in gluon
Faster modeling with gluon.nn.Sequential
Dropout regularization from scratch
Dropout regularization with gluon
Plumbing: A look under the hood of gluon
Designing a custom layer with gluon
Serialization - saving, loading and checkpointing

Convolutional neural networks

Convolutional neural networks from scratch
Convolutional Neural Networks in gluon
Deep convolutional neural networks
Very deep networks with repeating elements
Batch Normalization from scratch
Batch Normalization in gluon

Recurrent neural networks

Recurrent Neural Networks (RNNs) for Language Modeling
Long short-term memory (LSTM) RNNs
Gated recurrent unit (GRU) RNNs
Recurrent Neural Networks with gluon

Optimization

Introduction
Gradient descent and stochastic gradient descent from scratch
Gradient descent and stochastic gradient descent with Gluon
Momentum from scratch
Momentum with Gluon
Adagrad from scratch
Adagrad with Gluon
RMSprop from scratch
RMSprop with Gluon
Adadelta from scratch
Adadelta with Gluon
Adam from scratch
Adam with Gluon

High-performance and distributed training

Fast, portable neural networks with Gluon HybridBlocks
Training with multiple GPUs from scratch
Training on multiple GPUs with gluon
Distributed training with multiple machines

Computer vision

Object Detection Using Convolutional Neural Networks
Transfering knowledge through finetuning
Visual Question Answering in gluon

Natural language processing

Tree LSTM modeling for semantic relatedness

Recommender systems

Introduction to recommender systems

Time series

Linear Dynamical Systems with MXNet
Filtering
Generating Synthetic Dataset
Exponential Smoothing and Innovation State Space Model (ISSM)
Filtering

High-performance and distributed training

Variational Autoencoders with Gluon
Implementing a Gaussian VAE

Generative adversarial networks

Generative Adversarial Networks
Deep Convolutional Generative Adversarial Networks
Pixel to Pixel Generative Adversarial Networks

Variational methods

Bayes by Backprop from scratch (NN, classification)
Bayes by Backprop with gluon (NN, classification)

The Straight Dope

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