Getting started with TensorFlow 2

Welcome to the course on Getting started with TensorFlow 2! This module introduces the course and covers essential aspects such as TensorFlow documentation, installation, and upgrading from TensorFlow 1. Participants will also be guided through practical coding tutorials to gain hands-on experience with TensorFlow and Google Colab.

The Sequential model API module delves into the fundamentals of building deep learning models using the Sequential API. Participants will learn about Keras, building a Sequential model, convolutional and pooling layers, model compilation, fitting, evaluation, and prediction methods. Practical tutorials and programming assignments will reinforce the learning process.

The Validation, regularisation and callbacks module explores the critical aspects of model validation, regularisation, and callbacks. Topics include validation sets, model regularisation, introduction to callbacks, early stopping, and patience. Participants will apply their knowledge in practical programming assignments and a model validation project.

In the Saving and loading models module, participants will gain expertise in saving and loading model weights, model saving criteria, saving the entire model, loading pre-trained Keras models, and utilizing TensorFlow Hub modules. Hands-on tutorials and programming assignments will solidify the understanding of these concepts.

The Capstone Project module is the culmination of the course, where participants will apply their skills and knowledge to develop an image classifier deep learning model from scratch. This module offers a practical application of the concepts covered throughout the course.