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

Introduction

Let's say you're working with text data and your objective is to build a model that checks whether a sentence is grammatically correct. Consider the following sentence: "words? while sequence be this solved of can the ignoring". The question didn't make sense, right? Well, how about the following? "Can this be solved while ignoring the sequence of words?"

Suddenly, the text makes complete sense. What do we acknowledge, then, about working with text data? That sequence matters.

In the task of assessing whether a given sentence is grammatically correct, the sequence is important. Sequence-agnostic models would fail terribly at the task. The nature of the task requires you to analyze the sequence of the terms.

In the previous chapter, we worked with text data, discussing ideas around representation and creating our own word vectors. Text and natural language data have another important characteristic – they have a sequence...