Hands-On TensorBoard for PyTorch Developers

Rating 3.94 out of 5 (9 ratings in Udemy)

What you'll learn

Demonstrate TensorBoard visualizations with PyTorch models, including training curves, data distributions, data histograms, model graphs, and text embeddings
Log multiple parameters and events in PyTorch and easily use them for TensorBoard visualizations
Visualize numerous data types including scalar, vector, text, image, and audio data
View data and text embeddings in 2D and 3D
Use TensorBoard to detect errors and fix models …

Rating 3.94 out of 5 (9 ratings in Udemy)

What you'll learn

Demonstrate TensorBoard visualizations with PyTorch models, including training curves, data distributions, data histograms, model graphs, and text embeddings
Log multiple parameters and events in PyTorch and easily use them for TensorBoard visualizations
Visualize numerous data types including scalar, vector, text, image, and audio data
View data and text embeddings in 2D and 3D
Use TensorBoard to detect errors and fix models with hands-on examples in Machine Learning, image classification, and NLP
Track and optimize hyperparameter tuning so you can display model configurations and measure performance to compare multiple models and reproduce experiments
Log events from PyTorch with a few lines of code

Description

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.

About the Author

Joe Papa has an MSEE and over 23 years' experience in engineering R&D. He has led AI teams and developed Deep Learning models at Booz Allen and Perspecta Labs. Joe is also the founder of Mentorship .ai and has mentored hundreds of data scientists in Machine Learning, Deep Learning, and AI. He has taught over 6,000 students on Udemy in programming courses such as MATLAB.

Duration 2 Hours 58 Minutes

Paid

Self paced

Beginner Level

English (US)

Rating 3.94 out of 5 (9 ratings in Udemy)

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