This block will teach you how TensorBoard provides the visualization and tooling required for machine learning experimentation.

Callbacks

Callbacks are powerful tools that help customise a Keras model’s behaviour during training, evaluation, or inference.

Tensorboard

TensorBoard is a free and open-source tool that provides measurements and visualizations required during the machine learning workflow. This module will teach you how to use the TensorBoard library using Python for Machine Learning.

Graph Visualization and Visualizing weights, bias & gradients

In this module, you will learn everything you need to know about Graph Visualization and Visualizing weights, bias & gradients.

Hyperparameter tuning

This module will drive you through all the concepts involved in hyperparameter tuning, an automated model enhancer provided by AI training.

Occlusion experiment

Occlusion experiment is a method to determine which image patches contribute to the maximum level to the output of a neural network.

Saliency maps

A saliency map is an image, which displays each pixel’s unique quality. This module will cover how to use a saliency map in deep learning.

Neural style transfer

Neural style transfer is an optimization technique that takes two images, a content image and a style reference image, later blends them together. Now, the output image resembles the content image but displayed in the style of the style reference image.