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

Pooling Layers

Pooling layers are used to reduce the dimensions of the feature maps of convolution layers. But why do we need to perform such downsampling? One of the main reasons is to reduce the number of calculations that are performed in the networks. Adding multiple layers of convolution with different filters can have a significant impact on the training time. Also, reducing the dimensions of feature maps can eliminate some of the noise in the feature map and help us focus only on the detected pattern. It is quite typical to add a pooling layer after each convolutional layer in order to reduce the size of the feature maps.

A pooling operation acts very similarly to a filter, but rather than performing a convolution operation, it uses an aggregation function such as average or max (max is the most widely used function in the current CNN architecture). For instance, max pooling will look at a specific area of the feature map and find the maximum values of its pixels. Then,...

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