Train your networks faster with PyTorch
About This Video
- Build computational graphs on-the-fly using strong PyTorch skills and develop a solid foundation in neural network structures.
- The course is embedded with easy-to-follow instructions that will help you build your first dynamic graph.
- You will apply dynamic neural networks to solve various real-world problems using dynamic memory and dynamic computations.
Deep learning influences key aspects of core sectors such as IT, finance, science, and many more. Problems arise when it comes to getting computational resources for your network. You need to have a powerful GPU and plenty of time to train a network for solving a real-world task.
Dynamic neural networks help save training time on your networks. They also reduce the amount of computational resources required. In this course, you'll learn to combine various techniques into a common framework. Then you will use dynamic graph computations to reduce the time spent training a network.
By the end, you'll be ready to use the power of PyTorch to easily train neural networks of varying complexities.
This course uses Python 3.6, PyTorch 0.4 and CUDA Toolkit 7.5 while not the latest version available, it provides relevant and informative content for legacy users of Python.
Table of contents
- Chapter 1 : Getting Started with PyTorch
- Chapter 2 : Imperative Side of PyTorch
- Chapter 3 : Dynamic Computational Graphs: Intuition and Examples
- Chapter 4 : Creating Extensions with PyTorch
- Chapter 5 : Image Captioning: Why Dynamic Graph Is a Good Choice?
- Chapter 6 : Natural Language Processing: Intuition for Dynamic Programming
- Title: Dynamic Neural Network Programming with PyTorch
- Release date: January 2019
- Publisher(s): Packt Publishing
- ISBN: 9781789610314
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