Building Deep Learning Models with TensorFlow

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 advanced reinforcement learning tasks. You will be able to practice the concepts learned in the hands-on labs after each lesson. A culminating final project in the last module will provide you an opportunity to apply your knowledge to build a Regression Model in Keras. This course is suitable for all aspiring AI engineers who want to learn TensorFlow and Keras. It requires some basic knowledge of Python programming and basic mathematical concepts such as gradients and matrices.

• Introduction
• In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models. You will also learn about the fundamentals of Deep Learning.
• Supervised Learning Models
• In this module, you will learn about about Convolutional Neural Networks, and the building blocks of a convolutional neural network, such as convolution and feature learning. You will also learn about the popular MNIST database. Finally, you will learn how to build a Multi-layer perceptron and convolutional neural networks in Python and using TensorFlow.
• Supervised Learning Models (Cont'd)
• In this module, you will learn about the recurrent neural network model, and special type of a recurrent neural network, which is the Long Short-Term Memory model. Also, you will learn about the Recursive Neural Tensor Network theory, and finally, you will apply recurrent neural networks to language modelling.
• Unsupervised Deep Learning Models
• In this module, you will learn about the applications of unsupervised learning. You will learn about Restricted Boltzmann Machines (RBMs), and how to train an RBM. Finally, you will apply Restricted Boltzmann Machines to build a recommendation system.
• Unsupervised Deep Learning Models (Cont'd) and scaling
• In this module, you will mainly learn about autoencoders and their architecture.