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

LSTMs, GRUs, and Other Variants

The idea behind plain RNNs is very powerful and the architecture has shown tremendous promise. Due to this, researchers have experimented with the architecture of RNNs to find ways to overcome the one major drawback (the vanishing gradient problem) and exploit the power of RNNs. This led to the development of LSTMs and GRUs, which have now practically replaced RNNs. Indeed, these days, when we refer to RNNs, we usually refer to LSTMs, GRUs, or their variants.

This is because these variants are designed specifically to handle the vanishing gradient problem and learn long-range dependencies. Both approaches have outperformed plain RNNs significantly in most tasks around sequence modeling, and the difference is especially higher for long sequences. The paper titled Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (available at https://arxiv.org/abs/1406.1078) performs an empirical analysis of the performance...