Chainer is an open source framework based on Python, built for accelerating research and implementing neural network models. It provides flexible, efficient, and simplified approaches to developing deep learning algorithms.
This instructor-led, live training (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start developing neural network models.
- Define and implement neural network models using a comprehensible source code.
- Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- An understanding of artificial neural networks
- Familiarity with deep learning frameworks (Caffe, Torch, etc.)
- Python programming experience
- AI Researchers
- Chainer vs Caffe vs Torch
- Overview of Chainer features and components
- 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
Summary and Conclusion