Build better PyTorch models with TensorBoard visualization

About This Video

• Learn everything you need to know to start using TensorBoard in PyTorch with practical examples in Machine Learning, Image Classification, and Natural Language Processing (NLP)
• Launch TensorBoard from any developer environment, including Jupyter notebooks and Google Colab
• Visualize and optimize your PyTorch models using techniques such as model graphs, training curves, image data, text embeddings, and many more

In Detail

TensorBoard is a visualization library for TensorFlow that plots training runs, tensors, and graphs. TensorBoard has been natively supported since the PyTorch 1.1 release. In this course, you will learn how to perform Machine Learning visualization in PyTorch via TensorBoard. This course is full of practical, hands-on examples. You will begin with a quick introduction to TensorBoard and how it is used to plot your PyTorch training models. You will learn how to write TensorBoard events and run TensorBoard with PyTorch to obtain visualizations of the training progress of a neural network. You will visualize scalar values, images, text and more, and save them as events. You will log events in PyTorch–for example, scalar, image, audio, histogram, text, embedding, and back-propagation.

By the end of the course, you will be confident enough to use TensorBoard visualizations in PyTorch for your real-world projects.

Audience

This course targets developers, data scientists, analysts, and AI/ML engineers who work with PyTorch and want to leverage the power of the TensorBoard library to visualize the training progress of their neural networks.

Requirement: This course requires basic familiarity with Python and an IDE (Jupyter Notebooks or Colab), together with basic familiarity with PyTorch for testing and training neural networks.