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The Deep Learning Workshop
The Deep Learning Workshop

The Deep Learning Workshop: Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras

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Profile Icon Mirza Rahim Baig Profile Icon Thomas Joseph Profile Icon Anthony So Profile Icon Nipun Sadvilkar Profile Icon Mohan Kumar Silaparasetty +1 more Show less
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Arrow left icon
Profile Icon Mirza Rahim Baig Profile Icon Thomas Joseph Profile Icon Anthony So Profile Icon Nipun Sadvilkar Profile Icon Mohan Kumar Silaparasetty +1 more Show less
Arrow right icon
€32.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (4 Ratings)
Paperback Jul 2020 474 pages 1st Edition
eBook
€17.99 €26.99
Paperback
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Free Trial
Renews at €18.99p/m
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The Deep Learning Workshop

2. Neural Networks

Overview

This chapter starts with an introduction to biological neurons; we see how an artificial neural network is inspired by biological neural networks. We will examine the structure and inner workings of a simple single-layer neuron called a perceptron and learn how to implement it in TensorFlow. We will move on to building multilayer neural networks to solve more complex multiclass classification tasks and discuss the practical considerations of designing a neural network. As we build deep neural networks, we will move on to Keras to build modular and easy-to-customize neural network models in Python. By the end of this chapter, you'll be adept at building neural networks to solve complex problems.

Introduction

In the previous chapter, we learned how to implement basic mathematical concepts such as quadratic equations, linear algebra, and matrix multiplication in TensorFlow. Now that we have learned the basics, let's dive into Artificial Neural Networks (ANNs), which are central to artificial intelligence and deep learning.

Deep learning is a subset of machine learning. In supervised learning, we often use traditional machine learning techniques, such as support vector machines or tree-based models, where features are explicitly engineered by humans. However, in deep learning, the model explores and identifies the important features of a labeled dataset without human intervention. ANNs, inspired by biological neurons, have a layered representation, which helps them learn labels incrementally—from the minute details to the complex ones. Consider the example of image recognition: in a given image, an ANN would just as easily identify basic details such as light and...

Neural Networks and the Structure of Perceptrons

A neuron is a basic building block of the human nervous system, which relays electric signals across the body. The human brain consists of billions of interconnected biological neurons, and they are constantly communicating with each other by sending minute electrical binary signals by turning themselves on or off. The general meaning of a neural network is a network of interconnected neurons. In the current context, we are referring to ANNs, which are actually modeled on a biological neural network. The term artificial intelligence is derived from the fact that natural intelligence exists in the human brain (or any brain for that matter), and we humans are trying to simulate this natural intelligence artificially. Though ANNs are inspired by biological neurons, some of the advanced neural network architectures, such as CNNs and RNNs, do not actually mimic the behavior of a biological neuron. However, for ease of understanding, we will...

Training a Perceptron

To train a perceptron, we need the following components:

  • Data representation
  • Layers
  • Neural network representation
  • Loss function
  • Optimizer
  • Training loop

In the previous section, we covered most of the preceding components: the data representation of the input data and the true labels in TensorFlow. For layers, we have the linear layer and the activation functions, which we saw in the form of the net input function and the sigmoid function respectively. For the neural network representation, we made a function called perceptron(), which uses a linear layer and a sigmoid layer to perform predictions. What we did in the previous section using input data and initial weights and biases is called forward propagation. The actual neural network training involves two stages: forward propagation and backward propagation. We will explore them in detail in the next few steps. Let's look at the training process at a higher level:

    ...

Keras as a High-Level API

In TensorFlow 1.0, there were several APIs, such as Estimator, Contrib, and layers. In TensorFlow 2.0, Keras is very tightly integrated with TensorFlow, and it provides a high-level API that is user-friendly, modular, composable, and easy to extend in order to build and train deep learning models. This also makes developing code for neural networks much easier. Let's see how it works.

Exercise 2.05: Binary Classification Using Keras

In this exercise, we will implement a very simple binary classifier with a single neuron using the Keras API. We will use the same data.csv file that we used in Exercise 2.02, Perceptron as a Binary Classifier:

Note

The dataset can be downloaded from GitHub by accessing the following GitHub link: https://packt.live/2BVtxIf.

  1. Import the required libraries:
    import tensorflow as tf
    import pandas as pd
    import matplotlib.pyplot as plt
    %matplotlib inline
    # Import Keras libraries
    from tensorflow.keras.models import...

Exploring the Optimizers and Hyperparameters of Neural Networks

Training a neural network to get good predictions requires tweaking a lot of hyperparameters such as optimizers, activation functions, the number of hidden layers, the number of neurons in each layer, the number of epochs, and the learning rate. Let's go through each of them one by one and discuss them in detail.

Gradient Descent Optimizers

In an earlier section titled Perceptron Training Process in TensorFlow, we briefly touched upon the gradient descent optimizer without going into the details of how it works. This is a good time to explore the gradient descent optimizer in a little more detail. We will provide an intuitive explanation without going into the mathematical details.

The gradient descent optimizer's function is to minimize the loss or error. To understand how gradient descent works, you can think of this analogy: imagine a person at the top of a hill who wants to reach the bottom...

Activity 2.01: Build a Multilayer Neural Network to Classify Sonar Signals

In this activity, we will use the Sonar dataset (https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)), which has patterns obtained by bouncing sonar signals off a metal cylinder at various angles and under various conditions. You will build a neural network-based classifier to classify between sonar signals bounced off a metal cylinder (the Mine class), and those bounced off a roughly cylindrical rock (the Rock class). We recommend using the Keras API to make your code more readable and modular, which will allow you to experiment with different parameters easily:

Note

You can download the sonar dataset from this link https://packt.live/31Xtm9M.

  1. The first step is to understand the data so that you can figure out whether this is a binary classification problem or a multiclass classification problem.
  2. Once you understand the data and the type of classification that...

Summary

In this chapter, we started off by looking at biological neurons and then moved on to artificial neurons. We saw how neural networks work and took a practical approach to building single-layer and multilayer neural networks to solve supervised learning tasks. We looked at how a perceptron works, which is a single unit of a neural network, all the way to a deep neural network capable of performing multiclass classification. We saw how Keras makes it very easy to create deep neural networks with a minimal amount of code. Lastly, we looked at practical considerations to take into account when building a successful neural network, which involved important concepts such as gradient descent optimizers, overfitting, and dropout.

In the next chapter, we will go to the next level and build a more complicated neural network called a CNN, which is widely used in image recognition.

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Key benefits

  • 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

Description

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.

Who is this book for?

If you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.

What you will learn

  • 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
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Table of Contents

7 Chapters
1. Building Blocks of Deep Learning Chevron down icon Chevron up icon
2. Neural Networks Chevron down icon Chevron up icon
3. Image Classification with Convolutional Neural Networks (CNNs) Chevron down icon Chevron up icon
4. Deep Learning for Text – Embeddings Chevron down icon Chevron up icon
5. Deep Learning for Sequences Chevron down icon Chevron up icon
6. LSTMs, GRUs, and Advanced RNNs Chevron down icon Chevron up icon
7. Generative Adversarial Networks Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(4 Ratings)
5 star 50%
4 star 50%
3 star 0%
2 star 0%
1 star 0%
James Le Nov 03, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book makes it easy for you to understand deep learning with the help of interesting examples and exercises throughout. There are 7 chapters in total:- Chapter 1 discusses the practical applications of deep learning.- Chapter 2 teaches you the structure of artificial neural networks.- Chapter 3 covers image processing, how it works, and how that knowledge can be applied to Convolutional Neural Networks (CNNs).- Chapter 4 introduces you to the world of Natural Language Processing.- Chapter 5 shows you how to work on a classic sequence processing task—stock price prediction.- Chapter 6 reviews RNNs' practical drawbacks and how Long Short Term Memory (LSTM) models help overcome them.- Chapter 7 introduces you to generative adversarial networks (GANs) and their basic components.All the chapters provide hands-on exercises for you to work on (using TensorFlow 2.0 and Keras). With more than 400 pages of content, this is a comprehensive coverage of deep learning fundamentals from a programming-intensive perspective!
Amazon Verified review Amazon
Luis René Mata Quiñonez Nov 09, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a very good book on deep learning. It is very focused on the most important DL applications by using Keras and TensorFlow. It provides a hands-on approach where examples and models can be easily translated to solve different problems.
Amazon Verified review Amazon
Jackie K. Dec 13, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This is a decent intro to deep learning that covers Tensorflow and Keras. I was torn between 3 stars and 4. The content from what I can see is good from a nuts and bolts perspective, but there are a few negative aspects to the book. I will just cover one, because I think it is the most important. Many mistakes are made in science by a lack of representation. For example chatbots, don't recognize women's voices well, and there have also been issues with the recognition of various skin colors because models have been trained on bias datasets. None of these issues were brought up in the book even in passing with information on where to learn more. From a nuts and bolts perspective this book is a 4. From a comprehensive intro perspective this book is 3.
Amazon Verified review Amazon
Hakuna Matata Oct 17, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This is a good introductory book on deep learning - gives you a good tour of all the core fundamentals. Also, I loved the fact that you could try out the examples and code snippets using binger.org infrastructure - this way you don't have to mess with setting up your own environment to run and test the code snippets.One nit is no mention of any of the latest transformer architecture or modern language models such as BERT and RoBERTa.
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