Now that we've discussed what RNNs are, how to train them, and how to modify them for improved performance, let's build one! The next few sections will cover how we process and represent data for an RNN that uses LSTM units. We will also look at what the network itself looks like, the code for GRU units, and some tools for understanding what our network is doing, too.
- 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
Building an LSTM in Gorgonia
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