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.

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Table of Contents (9 chapters)
Preface
About the Book
1
1. Building Blocks of Deep Learning
1. Building Blocks of Deep Learning
Introduction
Introduction to TensorFlow
Summary
Free Chapter
2
2. Neural Networks
2. Neural Networks
Introduction
Neural Networks and the Structure of Perceptrons
Training a Perceptron
Keras as a High-Level API
Exploring the Optimizers and Hyperparameters of Neural Networks
Activity 2.01: Build a Multilayer Neural Network to Classify Sonar Signals
Summary
3
3. Image Classification with Convolutional Neural Networks (CNNs)
3. Image Classification with Convolutional Neural Networks (CNNs)
Introduction
Digital Images
Image Processing
Convolutional Neural Networks
Pooling Layers
Data Augmentation
Saving and Restoring Models
Transfer Learning
Fine-Tuning
Summary
4
4. Deep Learning for Text – Embeddings
4. Deep Learning for Text – Embeddings
Introduction
Deep Learning for Natural Language Processing
Classical Approaches to Text Representation
Distributed Representation for Text
Summary
5
5. Deep Learning for Sequences
5. Deep Learning for Sequences
Introduction
Working with Sequences
Recurrent Neural Networks
Summary
6
6. LSTMs, GRUs, and Advanced RNNs
6. LSTMs, GRUs, and Advanced RNNs
Introduction
Long-Range Dependence/Influence
The Vanishing Gradient Problem
Sequence Models for Text Classification
The Embedding Layer
Building the Plain RNN Model
Making Predictions on Unseen Data
LSTMs, GRUs, and Other Variants
Parameters in an LSTM
LSTM versus Plain RNNs
Gated Recurrence Units
Bidirectional RNNs
Stacked RNNs
Summarizing All the Models
Attention Models
More Variants of RNNs
Summary
7
7. Generative Adversarial Networks
7. Generative Adversarial Networks
Introduction
Deep Convolutional GANs
Summary
Appendix
1. Building Blocks of Deep Learning
2. Neural Networks
3. Image Classification with Convolutional Neural Networks (CNNs)
4. Deep Learning for Text – Embeddings
5. Deep Learning for Sequences
6. LSTMs, GRUs, and Advanced RNNs
7. Generative Adversarial Networks

Summary

In this chapter, we began by discussing the peculiarities of text data and how ambiguity makes NLP difficult. We discussed that there are two key ideas in working with text – preprocessing and representation. We discussed the many tasks involved in preprocessing, that is, getting your data cleaned up and ready for analysis. We saw various approaches to removing imperfections from the data.

Representation was the next big aspect – we understood the considerations in representing text and converting text into numbers. We looked at various approaches, beginning with classical approaches, which included one-hot encoding, the count-based approach, and the TF-IDF method.

Word embeddings are a whole new approach to representing text that leverage ideas from distributional semantics – terms that appear in similar contexts have similar meanings. The word2vec algorithm smartly exploits this idea by formulating a prediction problem: predict a target word given...

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