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

Attention Models

Attention models were first introduced in late 2015 by Dzmitry Bahdanau, KyungHyun Cho, and Yoshua Bengio in their influential and seminal paper (https://arxiv.org/abs/1409.0473) that demonstrated the state-of-the-art results of English-to-French translation. Since then, this idea has been used for many sequence-processing tasks with great success, and attention models are becoming increasingly popular. While a detailed explanation and mathematical treatment is beyond the scope of this book, let's understand the intuition behind the idea that is considered by many big names in the field of deep learning as a significant development in our approach to sequence modeling.

The intuition behind attention can be best understood using an example from the task it was developed for – translation. When a novice human translates a long sentence between languages, they don't translate the entire sentence in one go. They break the original sentence down into...