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

4. Deep Learning for Text – Embeddings

Overview

In this chapter, we will begin our foray into Natural Language Processing for text. We will start by using the Natural Language Toolkit to perform text preprocessing on raw text data, where we will tokenize the raw text and remove punctuations and stop words. As we progress through this chapter, we will implement classical approaches to text representation, such as one-hot encoding and the TF-lDF approach. This chapter demonstrates the power of word embeddings and explains the popular deep learning-based approaches for embeddings. We will use the Skip-gram and Continuous Bag of Words algorithms to generate our own word embeddings. We will explore the properties of the embeddings, the different parameters of the algorithms, and generate vectors for phrases. By the end of this chapter, you will be able to handle text data and start using word embeddings by using pre-trained models, as well as your own embeddings.