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Hands-On TensorBoard for PyTorch Developers

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Hands-On TensorBoard for PyTorch Developers, available at $44.99, has an average rating of 4.25, with 23 lectures, 5 quizzes, based on 11 reviews, and has 72 subscribers.

You will learn about 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 This course is ideal for individuals who are 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. It is particularly useful for 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.

Enroll now: Hands-On TensorBoard for PyTorch Developers

Summary

Title: Hands-On TensorBoard for PyTorch Developers

Price: $44.99

Average Rating: 4.25

Number of Lectures: 23

Number of Quizzes: 5

Number of Published Lectures: 23

Number of Published Quizzes: 5

Number of Curriculum Items: 28

Number of Published Curriculum Objects: 28

Original Price: $109.99

Quality Status: approved

Status: Live

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

Who Should Attend

  • 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.

Target Audiences

  • 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.

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.

Course Curriculum

Chapter 1: Introduction to TensorBoard

Lecture 1: Course Overview

Lecture 2: What Is TensorBoard and How Do We Leverage Its Power

Lecture 3: Running TensorBoard with PyTorch

Lecture 4: Running TensorBoard on Jupyter Notebooks and Google Colab

Chapter 2: Your First PyTorch Model with TensorBoard

Lecture 1: Simple Regression Example

Lecture 2: Visualizing Your Model Graph

Lecture 3: Training and Visualizing Loss Using TensorBoard

Lecture 4: Visualizing Data Summaries and Histograms

Lecture 5: Visualizing Other Data Types

Chapter 3: Image Classification and Model Development

Lecture 1: Hands-On Example: Image Classification

Lecture 2: Detect and Fix Errors with Model Graph Visualizations

Lecture 3: Visualize Training Loss and Other Metrics

Lecture 4: Visualize Image Data

Lecture 5: Display Confusion Matrix Using TensorBoard

Chapter 4: NLP Visualization and Model Experimentation

Lecture 1: Hands-On Example: NLP

Lecture 2: Visualizing Text Data

Lecture 3: Visualizing Word Embedding Using TensorBoard Projector

Lecture 4: Visualizing Model Graph – RNN

Lecture 5: Advanced Features and Limitations

Lecture 6: Advanced Features of TensorBoard and PyTorch Limitations

Chapter 5: Reviewing Your Visualizations and Models

Lecture 1: Visualizations Review

Lecture 2: Model Development Review

Lecture 3: What to do Next?

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 1 votes
  • 3 stars: 1 votes
  • 4 stars: 2 votes
  • 5 stars: 6 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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