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)
Preface

Deep Convolutional GANs

In the previous sections, where we implemented a GAN, we made use of an architecture based on the Multi-Layer Perceptron (MLP). As you may recall from the previous chapters, MLPs have fully connected layers. This implies that all the neurons in each layer have connections to all the neurons of the subsequent layer. For this reason, MLPs are also called fully connected layers. The GAN that we developed in the previous section can also be called a Fully Connected GAN (FCGAN). In this section, we will learn about another architecture called Deep Convolutional GANs (DCGANS). As the name implies, this is based on the Convolutional Neural Network (CNN) architecture that you learned about in Chapter 4, Deep Learning for Text – Embeddings. Let's revisit some of the building blocks of DCGANs.

Building Blocks of DCGANs

Most of the building blocks of DCGANs are similar to what you learned about when you were introduced to CNNs in Chapter 3, Image Classification...