Hands-On TensorBoard for PyTorch Developers

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Joe Papa

2:12:50

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1.Introduction to TensorBoard

01.Course Overview.mp4

05:05

02.What Is TensorBoard and How Do We Leverage Its Power.mp4

05:48

03.Running TensorBoard with PyTorch.mp4

05:13

04.Running TensorBoard on Jupyter Notebooks and Google Colab.mp4

06:54

2.Your First PyTorch Model with TensorBoard

05.Simple Regression Example.mp4

02:26

06.Visualizing Your Model Graph.mp4

05:13

07.Training and Visualizing Loss Using TensorBoard.mp4

05:50

08.Visualizing Data Summaries and Histograms.mp4

05:45

09.Visualizing Other Data Types.mp4

04:24

3.Image Classification and Model Development

10.Hands-On Example - Image Classification.mp4

02:42

11.Detect and Fix Errors with Model Graph Visualizations.mp4

09:05

12.Visualize Training Loss and Other Metrics.mp4

03:20

13.Visualize Image Data.mp4

03:36

14.Display Confusion Matrix Using TensorBoard.mp4

03:15

4.NLP Visualization and Model Experimentation

15.Hands-On Example - NLP.mp4

06:38

16.Visualizing Text Data.mp4

07:45

17.Visualizing Word Embedding Using TensorBoard Projector.mp4

13:35

18.Visualizing Model Graph RNN.mp4

06:03

19.Hyperparameter Tuning.mp4

15:48

20.Advanced Features and Limitations.mp4

03:31

5.Reviewing Your Visualizations and Models

21.Visualizations Review.mp4

02:55

22.Model Development Review.mp4

05:03

23.What to do Next.mp4

02:56

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

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TensorFlow

PyTorch

Joe Papa

Instructor's Courses

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.

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.