Training a model requires several important components. We have the inputs, but we also need to have a loss function and a way to interpret the outputs, as well as setting a few other hyperparameters for our model training process.
- 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
Training
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