Build, Train and Deploy ML Models with Keras on Google Cloud

Go to Course: https://www.coursera.org/learn/intro-tensorflow

Introduction

Design and build a TensorFlow input data pipeline.

Use the tf.data library to manipulate data in large datasets.

Use the Keras Sequential and Functional APIs for simple and advanced model creation.

Train, deploy, and productionalize ML models at scale with Vertex AI.

Syllabus

Introduction to the Course

This module provides an overview of the course and its objectives.

This module introduces the TensorFlow framework and previews its main components as well as the overall API hierarchy.

Data is the a crucial component of a machine learning model. Collecting the right data is not enough. You also need to make sure you put the right processes in place to clean, analyze and transform the data, as needed, so that the model can take the most signal of it as possible. In this module we discuss training on large datasets with tf.data, working with in-memory files, and how to get the data ready for training. Then we discuss embeddings, and end with an overview of scaling data with tf.keras preprocessing layers.

In this module, we discuss activation functions and how they are needed to allow deep neural networks to capture nonlinearities of the data. We then provide an overview of Deep Neural Networks using the Keras Sequential and Functional APIs. Next we describe model subclassing, which offers greater flexibility in model building. The module ends with a lesson on regularization.

In this module, we describe how to train TensorFlow models at scale using Vertex AI.

This module is a summary of the Build, Train, and Deploy ML Models with Keras on Google Cloud course.