Course Outline

Introduction

  • Chainer vs Caffe vs Torch
  • Overview of Chainer features and components

Getting Started

  • Understanding the trainer structure
  • Installing Chainer, CuPy, and NumPy
  • Defining functions on variables

Training Neural Networks in Chainer

  • Constructing a computational graph
  • Running MNIST dataset examples
  • Updating parameters using an optimizer
  • Processing images to evaluate results

Working with GPUs in Chainer

  • Implementing recurrent neural networks
  • Using multiple GPUs for parallelization

Implementing Other Neural Network Models

  • Defining RNN models and running examples
  • Generating images with Deep Convolutional GAN
  • Running Reinforcement Learning examples

Troubleshooting

Summary and Conclusion

Requirements

  • An understanding of artificial neural networks
  • Familiarity with deep learning frameworks (Caffe, Torch, etc.)
  • Python programming experience

Audience

  • AI Researchers
  • Developers
 14 Hours

Testimonials (4)

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