Table Of Contents
Table Of Contents


Getting started

A 60-minute Gluon crash course../../crash-course/index.html

Six 10-minute tutorials covering the core concepts of MXNet using the Gluon API.

Gluon - Neural network building blocks

An introduction to defining and training neural networks with Gluon.

Gluon: from experiment to deployment

An end to end tutorial on working with the MXNet Gluon API.

Custom Layers for Beginners

A guide to implementing custom layers for beginners.

Logistic regression using Gluon API explained

Implementing logistic regression using the Gluon API.

Saving and Loading Gluon Models

Saving and loading trained models.

Using pre-trained models in MXNet

Using pre-trained models with Apache MXNet.


Data Loadingdata.html

How to load data for training.

Image Augmentationimage-augmentation.html

Boost your training dataset with image augmentation.

Data Augmentation

A guide to data augmentation.

Gluon Datasets and DataLoader

A guide to loading data using the Gluon API.

NDArray - Scientific computing on CPU and GPU

A guide to the NDArray data structure.


Neural Networksnn.html

How to use Layers and Blocks.

Normalization Blocksnormalization/normalization.html

Understand usage of normalization layers (such as BatchNorm).

Activation Blocksactivations/activations.html

Understand usage of activation layers (such as ReLU).

Loss Functionsloss.html

How to use loss functions for predicting outputs.

Initializing Parametersinit.html

How to use the init function.

Parameter Managementparameters.html

How to manage parameters.

Learning Rate Finder

How to use the Learning Rate Finder to find a good learning rate.

Learning Rate Schedules

How to schedule Learning Rate change over time.


How to update neural network parameters using an optimization method.

Autograd API../autograd/autograd.html

How to use Automatic Differentiation with the Autograd API.

Advanced Topics


Best practices for the naming of things.

Custom Layerscustom-layer.html

A guide to implementing custom layers.

Custom Operators

Building custom operators with numpy.

Custom Losscustom-loss/custom-loss.html

A guide to implementing custom losses.

Gotchas using NumPy in Apache MXNet

Common misconceptions when using NumPy in Apache MXNet.

Hybrid- Faster training and easy deployment

Combines declarative and imperative programming using HybridBlock.


Speed up training with hybrid networks.

Advanced Learning Rate Schedules

Advanced exploration of Learning Rate shapes.

Applications Topics

Image Tutorialsimage/index.html

How to create deep learning models for images.

Text Tutorialstext/index.html

How to create deep learning models for text.