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Dynamic Neural Network Programming with PyTorch [Video]

More Information
  • Get familiar with PyTorch fundamentals while learning to code a deep neural network in Python
  • Create any task-oriented extension very quickly with the easy-to-use PyTorch interface
  • Perform image captioning and grammar parsing using Natural Language Processing
  • Use a computational graph and run it in parallel in the target GPU
  • Create unique C++/CUDA extensions for PyTorch that work on CPU and GPU
  • Use powerful toolkits from Python library while solving NLP or image recognition tasks

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.

All the related code files are placed on GitHub repository at https://github.com/PacktPublishing/-Dynamic-Neural-Network-Programming-with-PyTorch

Style and Approach

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.

  • 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.
Course Length 3 hours 6 minutes
Date Of Publication 31 Jan 2019


Anastasia Yanina

Anastasia Yanina is a Senior Data Scientist with around 5 years' experience. She is an expert in Deep Learning and Natural Language processing and constantly develops her skills as far as possible. She is passionate about human-to-machine interactions. She believes that bridging the gap may become possible with deep neural network architectures.
LinkedIn: https://www.linkedin.com/in/anastasia-ianina-b84ab4154/