Now that we've done all that work, let's explore some of the advantages of using a GPU for deep learning. First, let's go through how to actually get your application to use CUDA, and then we'll go through some of the CPU and GPU speeds.
Performance benchmarking of CPU versus GPU models for training and inference
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
Performance benchmarking of CPU versus GPU models for training and inference
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