Overview of this book
<p>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.</p>
<p>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. <br />By the end, you'll be ready to use the power of PyTorch to easily train neural networks of varying complexities.</p>
<p>All the related code files are placed on GitHub repository at <a href="https://github.com/PacktPublishing/-Dynamic-Neural-Network-Programming-with-PyTorch" target="_blank">https://github.com/PacktPublishing/-Dynamic-Neural-Network-Programming-with-PyTorch</a></p>
<h1>Style and Approach</h1>
<p>The course allows you to directly put into practice all the knowledge you've acquired. Throughout the course, we'll build a simple C++/CUDA extension with step-by-step instructions and complete two mini-projects: applying dynamic neural networks to image recognition and NLP-oriented problems (grammar parsing). Coding tips and hints are provided as well as illustrative examples and clear instructions to all the mini-projects. Short quizzes at the end of each lecture will ensure you've mastered it and check your progress.</p>
