Introduction

This section shows examples of working with neural networks in the PuzzleLib. The list of training materials is as follows:

1. Getting started

   1. PuzzleLib in a nutshell
   2. What is the theory behind - automatic differentiation
      • Introduction
      • Restoring a linear function with a single neuron
      • Restoring a linear function by a neural network
      • Restoration of a nonlinear function by a neural network with activation
   3. Training the MNIST classifier
   4. Pausing and resuming network training
   5. Network optimization

2. Application areas

   1. Computer vision
      • Training the CIFAR classifier
   2. Speech processing
      • Speech recognition with Wav2Letter

3. Additional options

   1. Interaction with other frameworks
      • Switching from TensorFlow
      • Switching from PyTorch
      • Converting to TensorRT engine
   2. Contents of the PuzzleLib module on the example of a linear layer
   3. Use of different backends
   4. Parallelize your computing