The objective of this section is to give the reader an understanding of how to implement Deep Neural Network architectures.
The following chapters are included in this section:
Section 2: Implementing Deep Neural Network Architectures
by Darrell Chua, Gareth Seneque
Hands-On Deep Learning with Go
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Section 1: Deep Learning in Go, Neural Networks, and How to Train Them
- Introduction to Deep Learning in Go
- What Is a Neural Network and How Do I Train One?
- Beyond Basic Neural Networks - Autoencoders and RBMs
- CUDA - GPU-Accelerated Training
- Section 2: Implementing Deep Neural Network Architectures
- Next Word Prediction with Recurrent Neural Networks
- Object Recognition with Convolutional Neural Networks
- Maze Solving with Deep Q-Networks
- Generative Models with Variational Autoencoders
- Section 3: Pipeline, Deployment, and Beyond!
- Building a Deep Learning Pipeline
- Scaling Deployment
- Other Books You May Enjoy
Section 2: Implementing Deep Neural Network Architectures
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