Hands-On-TensorBoard-for-PyTorch-Developers

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Hands-On-TensorBoard-for-PyTorch-Developers

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Hands-On TensorBoard for PyTorch Developers, Published by Packt

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# Hands-On-TensorBoard-for-PyTorch-Developers [Video]
This is the code repository for [Hands-On TensorBoard for PyTorch Developers [Video]]( https://www.packtpub.com/data/hands-on-tensorboard-for-pytorch-developers-video), published by [Packt](https://www.packtpub.com/?utm_source=github ). It contains all the supporting project files necessary to work through the video course from start to finish.

## About the Video Course
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.<br/>

<H2>What You Will Learn</H2>
<DIV class>

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

## Instructions and Navigation
### Assumed Knowledge
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.

## Technical Requirements <br/>
#### Software Requirements<br/>
• Operating system: any<br/>
• Browser: any modern browser<br/>
• Any Python IDE, Jupyter Notebook or Google Colab<br/>
• Tensorboard 2.1 or higher, PyTorch 1.3 or higher
<br/>

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