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Hands-On Neural Networks
Hands-On Neural Networks

Hands-On Neural Networks: Learn how to build and train your first neural network model using Python

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Profile Icon Leonardo De Marchi Profile Icon Laura Mitchell
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Hands-On Neural Networks

Getting Started with Supervised Learning

Artificial Intelligence (AI) is now a buzzword that is added to products and services to make them more appealing, and more often than not, it's a marketing strategy rather than a technical achievement. Most of the time, AI is used as an umbrella term to describe anything from simple analytics to advanced learning algorithms. It's something that sells, as most of the population does not have much knowledge about it, but intuitively everyone now understands that it is something that will change the world we live in.

Luckily, it's not just hype, and we have seen many astonishing achievements made by AI, such as Tesla's self-driving cars. Using recent research into deep neural networks, Tesla managed to create a functionality and made it available to the masses much quicker than most of the experts predicted.

In this book...

History of AI

The idea of AI, entailing machine that can think without human help, is surprisingly old. It can be dated back to the Indian philosophies of Charvaka, from around 1,500 BC.

The basis of AI is the philosophical concept that human reasoning can be mapped into a mechanical process. We can find this process in many civilizations in the first millennium BC, in particular in Greek philosophers such as Aristotle and Euclid.

Philosophers and mathematicians, such as Leibniz and Hobbes, in the 17th century explored the possibility that all of a human being's rational thoughts could be mapped into an algebraic or geometric system.

Only at the beginning of the 20th century was the limits defined of what mathematics and logic can accomplish and how far mathematical reasoning can be abstracted. It was at that time that the mathematician Alan Turing defined the Turing machine...

An overview of machine learning

ML is a variegated field with many different types of algorithms that try to learn in slightly different ways. We can divide them into the following different categories according to the way the algorithm performs the learning:

  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning
  • Reinforcement learning

In this book, we are going to touch on each single category, but the main focus will be supervised learning. We are going to briefly introduce these categories and the type of problems that they help solve.

Supervised learning

Supervised learning is nowadays the most common form of ML applied to business processes. These algorithms try to find a good approximation of the function...

Environment setup

There are only a few viable programming language options when creating ML software. The most popular ones are Python and R, but Scala is also quite popular. There are other languages, but the better ones in terms of use in ML are Julia, JavaScript, Java, and a few others. In this book, we will be using Python only. The motivation behind this choice is its widespread adoption, its simplicity of use, and the vast ecosystem of libraries that are possible to use.

In particular, we will be using Python 3.7 and a few of its following libraries:

  • numpy: For fast vectorized numerical computation
  • scipy: Built on top of numpy, with many mathematical functionalities
  • pandas: For data manipulation
  • scikit-learn: The main Python library for ML
  • tensorflow: The engine that powers our deep learning algorithms
  • keras: The library we are going to use to develop our deep learning...

Supervised learning in practice with Python

As we said earlier, supervised learning algorithms learn to approximate a function by mapping inputs and outputs to create a model that is able to predict future outputs given unseen inputs.

It's conventional to denote inputs as x and outputs as y; both can be numerical or categorical.

We can distinguish them as two different types of supervised learning:

  • Classification
  • Regression

Classification is a task where the output variable can assume a finite amount of elements, called categories. An example of classification would be classifying different types of flowers (output) given the sepal length (input). Classification can be further categorized in more sub types:

  • Binary classification: The task of predicting whether an instance belongs either to one class or the other
  • Multiclass classification: The task (also known as multinomial...

Feature engineering

Feature engineering is the process of creating new features by transforming existing ones. It is very important in traditional ML but is less important in deep learning.

Traditionally, the data scientists or the researchers would apply their domain knowledge and come up with a smart representation of the input that would highlight the relevant feature and make the prediction task more accurate.

For example, before the advent of deep learning, traditional computer vision required custom algorithms that were extracting the most relevant features, such as edge detection or Scale-Invariant Feature Transform (SIFT).

To understand this concept, let's look at an example. Here, we see an original photo:

And, after some feature engineering—in particular, after running an edge detection algorithm, we get the following result:

One of the great advantages...

Supervised learning algorithms

There are a lot of algorithms at our disposal for supervised learning. We choose the algorithm based on the task and the data we have at our disposal. If we don't have much data and there is already some knowledge around our problem, deep learning is probably not the best approach to start with. We should rather try simpler algorithms and come up with relevant features based on the knowledge we have.

Starting simple is always a good practice; for example, for categorization, a good starting point can be a decision tree. A simple decision tree algorithm that is difficult to overfit is random forest. It also gives good results out of the box. For regression problems, linear regression is still very popular, especially in domains, where it's necessary to justify the decision taken. For other problems, such as recommender systems, a good starting...

Summary

In this chapter, we learned about AI and deep learning. We also dived deep into understanding the various types of machine learning. Then, we learned how to set up our working environment and executed a supervised learning practice in Python. We also looked into feature engineering and supervised learning algorithms and how to use the right metrics to evaluate a model.

In the next chapter, we will learn the building blocks of deep learning and the math behind it.

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Key benefits

  • Explore neural network architecture and understand how it functions
  • Learn algorithms to solve common problems using backpropagation and perceptrons
  • Understand how to apply neural networks to applications with the help of useful illustrations

Description

Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics. Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks. By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions.

Who is this book for?

If you are interested in artificial intelligence and deep learning and want to further your skills, then this intermediate-level book is for you. Some knowledge of statistics will help you get the most out of this book.

What you will learn

  • Learn how to train a network by using backpropagation
  • Discover how to load and transform images for use in neural networks
  • Study how neural networks can be applied to a varied set of applications
  • Solve common challenges faced in neural network development
  • Understand transfer learning concepts to solve tasks using Keras and Visual Geometry Group (VGG) network
  • Get up to speed with advanced and complex deep learning concepts such as LSTMs and natural language processing (NLP)
  • Explore innovative algorithms including GANs and deep reinforcement learning
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Table of Contents

15 Chapters
Section 1: Getting Started Chevron down icon Chevron up icon
Getting Started with Supervised Learning Chevron down icon Chevron up icon
Neural Network Fundamentals Chevron down icon Chevron up icon
Section 2: Deep Learning Applications Chevron down icon Chevron up icon
Convolutional Neural Networks for Image Processing Chevron down icon Chevron up icon
Exploiting Text Embedding Chevron down icon Chevron up icon
Working with RNNs Chevron down icon Chevron up icon
Reusing Neural Networks with Transfer Learning Chevron down icon Chevron up icon
Section 3: Advanced Applications Chevron down icon Chevron up icon
Working with Generative Algorithms Chevron down icon Chevron up icon
Implementing Autoencoders Chevron down icon Chevron up icon
Deep Belief Networks Chevron down icon Chevron up icon
Reinforcement Learning Chevron down icon Chevron up icon
Whats Next? Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
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3 star 0%
2 star 50%
1 star 0%
Al Oct 11, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent and comprehensive insight! I love the author's big picture view and how it translated it in simple python code.
Amazon Verified review Amazon
Saadallah Kassir Jan 12, 2021
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
- The topics discussed are interesting and useful, although some chapters need additional explanation. Some of the concepts are indeed discussed at a very high level.- The notation may be a bit hard to follow as variables are sometimes undefined. Some terminology is not technically correct, e.g., confusion between MSE and RMSE, with a confusing expression.- The code snippets may be confusing, as some instructions may be irrelevant, e.g., instructions defining variables that are never used.- The book is printed in grayscale, which makes it harder to read the figures, although the authors indicated that all the figures are available in color online.- Several typos/missing words make some paragraphs hard to read.Overall, I like the idea of the book, but it may be worth waiting for the issue of a new edition, as several errors may be confusing to the reader.
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