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Home > Data > Neural Networks > Hands-On TensorBoard for PyTorch Developers [Video]
Hands-On TensorBoard for PyTorch Developers [Video]
Hands-On TensorBoard for PyTorch Developers [Video]

Hands-On TensorBoard for PyTorch Developers: Leverage the power of TensorBoard to visualize and optimize your PyTorch neural networks

Profile Icon Joe Papa
By Joe Papa
$130.99
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1 (1 Ratings)
Video Mar 2020 2 hours 13 minutes 1st Edition
Video
$130.99
Subscription
Free Trial
Renews at $19.99p/m
Profile Icon Joe Papa
By Joe Papa
$130.99
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1 (1 Ratings)
Video Mar 2020 2 hours 13 minutes 1st Edition
Video
$130.99
Subscription
Free Trial
Renews at $19.99p/m
Video
$130.99
Subscription
Free Trial
Renews at $19.99p/m

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Key benefits

  • 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

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. All relevant code files are placed on a GitHub repository at: https://github.com/PacktPublishing/Hands-On-TensorBoard-for-PyTorch-Developers

What you will 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

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 31, 2020
Length 2 hours 13 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781838983604
Category :
Data
Languages :
Python (Intermediate)
Concepts :
Neural Networks
Tools :
PyTorch (Intermediate) , TensorFlow (Intermediate)

What do you get with a video?

Product feature icon Download this video in MP4 format
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want

Product Details

Publication date : Mar 31, 2020
Length 2 hours 13 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781838983604
Category :
Data
Languages :
Python (Intermediate)
Concepts :
Neural Networks
Tools :
PyTorch (Intermediate) , TensorFlow (Intermediate)

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Table of Contents

5 Chapters
1. Introduction to TensorBoard Chevron down icon Chevron up icon
Course Overview
What Is TensorBoard and How Do We Leverage Its Power
Running TensorBoard with PyTorch
Running TensorBoard on Jupyter Notebooks and Google Colab
2. Your First PyTorch Model with TensorBoard Chevron down icon Chevron up icon
Simple Regression Example
Visualizing Your Model Graph
Training and Visualizing Loss Using TensorBoard
Visualizing Data Summaries and Histograms
Visualizing Other Data Types
3. Image Classification and Model Development Chevron down icon Chevron up icon
Hands-On Example: Image Classification
Detect and Fix Errors with Model Graph Visualizations
Visualize Training Loss and Other Metrics
Visualize Image Data
Display Confusion Matrix Using TensorBoard
4. NLP Visualization and Model Experimentation Chevron down icon Chevron up icon
Hands-On Example: NLP
Visualizing Text Data
Visualizing Word Embedding Using TensorBoard Projector
Visualizing Model Graph – RNN
Hyperparameter Tuning
Advanced Features and Limitations
5. Reviewing Your Visualizations and Models Chevron down icon Chevron up icon
Visualizations Review
Model Development Review
What to do Next?

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The code doesn't work. Pytorch, NVidia, or someone has upgraded their software and it is broken running with Windows 10. It is a known issue but as yet no solution for either Visual Studio 2017, VS2019 and VScode. I would like to see some discussion on the books github page but so far nothing.
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About the author

Profile icon Joe Papa
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.
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