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
Testimonials (4)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python
It felt like we were going through directly relevant information at a good pace (i.e. no filler material)
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to the use of neural networks
I liked the opportunities to ask questions and get more in depth explanations of the theory.