Overview

Deep learning is revolutionizing many fields, including computer vision, natural language processing, and robotics. In addition, Keras, a high-level neural networks API written in Python, has become an essential part of TensorFlow, making deep learning accessible and straightforward. Mastering these techniques will open many opportunities in research and industry. You will learn to create custom layers and models in Keras and integrate Keras with TensorFlow 2.x for enhanced functionality. You will develop advanced convolutional neural networks (CNNs) using Keras. You will also build transformer models for sequential data and time series using TensorFlow with Keras. The course also covers the principles of unsupervised learning in Keras and TensorFlow for model optimization and custom training loops. Finally, you will develop and train deep Q-networks (DQNs) with Keras for reinforcement learning tasks (an overview of Generative Modeling and Reinforcement Learning is provided). You will be able to practice the concepts learned using hands-on labs in each lesson. A culminating final project in the last module will provide you an opportunity to apply your knowledge to build a Classification Model using transfer learning. This course is suitable for all aspiring AI engineers who want to learn TensorFlow and Keras. It requires a working knowledge of Python programming and basic mathematical concepts such as gradients and matrices, as well as fundamentals of Deep Learning using Keras.

Syllabus

Advanced Keras Functionalities

This module provides an overview of Keras advanced features. It will cover Keras functional API for complex model creation. It also includes the creation of custom layers and models in Keras. Then the module describes the integration of Keras with TensorFlow 2.x for enhanced functionality. You will apply your learnings in labs and test your concepts in quizzes.

Advanced CNNs in Keras

In this module, you will learn to develop advanced convolutional neural networks (CNNs) using Keras. You will learn data augmentation techniques with Keras. In addition, you will implement transfer learning with Keras and leverage pre-trained models. Finally, you will learn how to use TensorFlow for enhancing image processing capabilities. You will apply your learnings in labs and test your concepts in quizzes.

Transformers in Keras

This module covers building and training advanced Transformers using Keras. You will further develop Transformer models for sequential data and time series using TensorFlow with Keras. In addition, you will learn to implement advanced Transformer techniques for text generation. You will apply your learnings in labs and test your concepts in quizzes.

Unsupervised Learning and Generative Models in Keras

In this module, you will learn the principles of unsupervised learning in Keras. You will learn to build and train autoencoders and diffusion models. In addition, you will develop generative adversarial networks (GANs) using Keras and integrate TensorFlow for advanced unsupervised learning tasks. You will apply your learnings in labs and test your concepts in quizzes.

Advanced Keras Techniques

In this module, you will learn advanced techniques in Keras for model development. You will create custom training loops and optimize models using Keras and perform hyperparameter tuning with Keras Tuner. Finally, you will learn to use TensorFlow for model optimization and custom training loops. You will apply your learnings in labs and test your concepts in quizzes.

Introduction to Reinforcement Learning with Keras

In this module, you will learn the fundamentals of reinforcement learning and its applications in Keras. The module also covers the Q-Learning algorithms using Keras. You will develop and train deep Q-networks (DQNs) with Keras for advanced reinforcement learning tasks. You will apply your learnings in labs and test your concepts in quizzes.