Dynamic Neural Network Programming with PyTorch
.MP4, AVC, 380 kbps, 1920x1080 | English, AAC, 160 kbps, 2 Ch | 3h 6m | 725 MB
Instructor: Anastasia Yanina

Train your networks faster with PyTorch
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.
What You Will Learn
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
More Info

https://www.packtpub.com/application-development/dynamic-neural-network-programming-pytorch-video