Book Image

The Deep Learning Workshop

By : Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So
Book Image

The Deep Learning Workshop

By: Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So

Overview of this book

Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You’ll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you’ll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you’ll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you’ll have learned the skills essential for building deep learning models with TensorFlow and Keras.
Table of Contents (9 chapters)


The power of creativity was always the exclusive domain of the human mind. This was one of the facts touted as one of the major differences between the human mind and the artificial intelligence domain. However, in the recent past, deep learning has been making baby steps in the path to being creative. Imagine you were at the Sistine Chapel in the Vatican and were looking up with bewilderment at the frescos immortalized by Michelangelo, wishing your deep learning models were able to recreate something like that. Well, maybe 10 years back, people would have scoffed at your thought. Not anymore, though – deep learning models have made great strides in regenerating immortal works. Applications like these are made possible by a class of networks called Generative Adversarial Networks (GANs).

Many applications have been made possible with GANs. Take a look at the following image:

Figure 7.1: Image translation using GANs


The preceding...