The Deep Learning Workshop

• Understand how deep learning, machine learning, and artificial intelligence are different
• Develop multilayer deep neural networks with TensorFlow
• Implement deep neural networks for multiclass classification using Keras
• Train CNN models for image recognition
• Handle sequence data and use it in conjunction with RNNs
• Build a GAN to generate high-quality synthesized images

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.

• Understand how to implement deep learning with TensorFlow and Keras
• Learn the fundamentals of computer vision and image recognition
• Study the architecture of different neural networks

Table of contents

1 1. Building Blocks of Deep Learning
Introduction
Introduction to TensorFlow
Summary

2 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)
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
Introduction
Deep Learning for Natural Language Processing
Classical Approaches to Text Representation
Distributed Representation for Text
Summary

5 5. Deep Learning for Sequences
Introduction
Working with Sequences
Recurrent Neural Networks
Summary

6 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
Introduction
Deep Convolutional GANs
Summary