English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 13m | 448 MB
Build better PyTorch models with TensorBoard visualization
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.
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
Table of Contents
Introduction to TensorBoard
1 Course Overview
2 What Is TensorBoard and How Do We Leverage Its Power
3 Running TensorBoard with PyTorch
4 Running TensorBoard on Jupyter Notebooks and Google Colab
Your First PyTorch Model with TensorBoard
5 Simple Regression Example
6 Visualizing Your Model Graph
7 Training and Visualizing Loss Using TensorBoard
8 Visualizing Data Summaries and Histograms
9 Visualizing Other Data Types
Image Classification and Model Development
10 Hands-On Example – Image Classification
11 Detect and Fix Errors with Model Graph Visualizations
12 Visualize Training Loss and Other Metrics
13 Visualize Image Data
14 Display Confusion Matrix Using TensorBoard
NLP Visualization and Model Experimentation
15 Hands-On Example – NLP
16 Visualizing Text Data
17 Visualizing Word Embedding Using TensorBoard Projector
18 Visualizing Model Graph – RNN
19 Hyperparameter Tuning
20 Advanced Features and Limitations
Reviewing Your Visualizations and Models
21 Visualizations Review
22 Model Development Review
23 What to do Next
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