Essentials of Deep Learning and AI

Experience Unsupervised Learning, Autoencoders, Feature Engineering, and Time Series Analysis with TensorFlow, Keras, and scikit-learn

Shashidhar Soppin , B N Chandrashekar, Dr. Manjunath Ramachandra

• Éditeur: BPB Publications
• Année: 2021
• ISBN:
  9789391030353

Drives next generation path with latest design techniques and methods in the fields of AI and Deep Learning

Key Features
? Extensive examples of Machine Learning and Deep Learning principles.
? Includes graphical demonstrations and visual tutorials for various libraries, configurations, and settings.
? Numerous use cases with the code snippets and examples are presented.

Description
'Essentials of Deep Learning and AI' curates the essential knowledge of working on deep neural network techniques and advanced machine learning concepts. This book is for those who want to know more about how deep neural networks work and advanced machine learning principles including real-world examples.This book includes implemented code snippets and step-by-step instructions for how to use them. You'll be amazed at how SciKit-Learn, Keras, and TensorFlow are used in AI applications to speed up the learning process and produce superior results. With the help of detailed examples and code templates, you'll be running your scripts in no time. You will practice constructing models and optimise performance while working in an AI environment.Readers will be able to start writing their programmes with confidence and ease. Experts and newcomers alike will have access to advanced methodologies. For easier reading, concept explanations are presented straightforwardly, with all relevant facts included.

What you will learn
? Learn feature engineering using a variety of autoencoders, CNNs, and LSTMs.
? Get to explore Time Series, Computer Vision and NLP models with insightful examples.
? Dive deeper into Activation and Loss functions with various scenarios.
? Get the experience of Deep Learning and AI across IoT, Telecom, and Health Care.
? Build a strong foundation around AI, ML and Deep Learning principles and key concepts.

Who this book is for
This book targets Machine Learning Engineers, Data Scientists, Data Engineers, Business Intelligence Analysts, and Software Developers who wish to gain a firm grasp on the fundamentals of Deep Learning and Artificial Intelligence. Readers should have a working knowledge of computer programming concepts.

Table of Contents
1. Introduction
2. Supervised Machine Learning
3. System Analysis with Machine Learning/Un-Supervised Learning
4. Feature Engineering
5. Classification, Clustering, Association Rules, and Regression
6. Time Series Analysis
7. Data Cleanup, Characteristics and Feature Selection
8. Ensemble Model Development
9. Design with Deep Learning
10. Design with Multi Layered Perceptron (MLP)
11. Long Short Term Memory Networks
12. Autoencoders
13. Applications of Machine Learning and Deep Learning
14. Emerging and Future Technologies.

CHAPTER 1

Introduction

The usage of different instruments and equipment by mankind to run the day-to-day activities dates back to the Stone Age. Since then, tens of thousands of devices have evolved to cater for household activities, infrastructure, healthcare, entertainment, communication, transportation, and the like. While most of these devices are dumb, smart devices such as personal assist has started to hit the market. The vision of such devices is not new and have found their place in the stories and legends. The magic lamp of Aladdin to the robots of Isaac Asimov has led to several inventions. Over a period of time, the “dumb” devices have been infused with the required intelligence to understand the surroundings and act accordingly. For example, a fan gets switched on when the ambient temperature exceeds 35 degree Celsius. Today, the expectation of people has increased several folds. The devices are required to understand not only the surroundings but also the end-user! They need to consider the static preferences of the users as well as the dynamic emotional state. This requirement led to large-scale research in data processing and the related fields under the term Artificial Intelligence and resulted in a massive proliferation of smart devices.

Structure

In this chapter, we will discuss the following topics:

• Artificial intelligence
  • What is Artificial Intelligence?
  • Definitions of AI
  • Applications of AI
  • Use cases of AI
  • Broad classification of what is AI, ML, FL, and DL?
• Machine learning
  • History and definitions of ML
  • ML and its applications
  • Classification of ML algorithms
• Deep Learning
  • Prerequisite to understand deep learning
  • Difference between machine learning & deep learning
• Tools and frameworks for AI, ML, and DL
• Languages used for AI, ML, and DL
• Datasets for AI, ML, and DL development

Objectives

After studying this chapter, you should be able to:

• Understand the concepts of AI, ML, DL and their components
• History of AI, ML, and DL
• Defined AI, ML, DL along with some examples and applications
• Benefits of using AI, ML, and DL in overall
• Programming languages used for ML & DL
• Standard tools used for ML & DL
• Reference database used for model development

1.1 Artificial intelligence

Making computers to “think like humans” was the main motivation behind the development of Artificial Intelligence. With this goal in mind, many pioneers as described later in this chapter, have contributed to AI and its advancement. Many advancements in the field of computer science also helped AI indirectly. All these put together, AI prospered over a period of years.

1.1.1 What is Artificial Intelligence?

Artificial Intelligence (AI) is the interdisciplinary umbrella term that spans the tools and techniques required to incorporate human-like intelligence into a system. The system can be a piece of software such as a dialog system and a hardware component such as a smart Internet of Things (IoT) or a machine such as a robotic arm. Alan Turing has defined the test criteria for a device or a program to be called as intelligent, to indicate how closely they can imitate human beings.

We are in the era of the “information age” (also sometimes called as “Computer age”, “Digital Age” or “New media age”), this is the golden age of modern human history. From taking selfies to storing files in the cloud, the growth of IoT based devices, and social media usage, we deal with quintillion bytes of data each day. The amount of data each individual produces in the 20th century is mind-boggling and this will be a whopping number. As of late 2019, it is estimated that human beings along generated approximately 2.5 quintillion bytes of data.

This sudden rise of data, which the human brain has to deal with, will be quite challenging and cumbersome. Most of the data analytics now is done by artificial intelligence-based machines/systems, because of this huge data gathering and lots of insights are generated which assist humans in many predictive trends that are arising in the business and software industry.

1.1.2 Definitions of Artificial Intelligence

AI has evolved from the 1950 of Alan Turing days to date. So many definitions of AI came up all these years. To make things simpler and easier, all the standard definitions are captured of AI from the pioneers of this industry. The following table explains the evolution of AI, the people who coined/defined the AI definition terminology during which year is described and when along with links.

Index	Year	AI definition	Defined By	Ref: Link
1	1950	Alan Turing didn't define what is AI. But while carrying out the so popularly called "The Turing Test" he came up with the question "whether machines can think?"	Alan Turing	https://plato.stanford.edu/entries/turing-test/ https://www.csee.umbc.edu/courses/471/papers/turing.pdf
2	1978	“The automation of activities that we associate with human thinking, activities such as decision-making problem-solving learning”	Bellman	https://archive.org/details/ AnIntroductionToArtificialIntelligence/page /n11/mode/2up
3	1985	“The exciting new effort to make computers think…machines with minds, in the full and literal sense.”	Haugeland	https://mitpress.mit.edu/books/artificial-intelligence-1
4	1985	“The study of mental faculties through the use of computational models”	Charniak &McDermott	https://dl.acm.org/doi/book/10.5555/30426
5	1990	“The art of creating machines that perform functions that require intelligence when performed by people”	Kurzweil
6	1992	“The study of the computations that make it possible to perceive, reason and act”	Winston
7	1998	“Computational Intelligence is the study of the design intelligent agents” “AI….is concerned with intelligent behavior in artifacts”	Poole et.al Nilsson

Table 1.1

When we say artificial intelligence, immediately a question comes to our mind, “Can computers think like human beings?” Many of the above definitions try to answer this question briefly. But one more pair of recent year’s pioneers of the AI industry, Peter Norvig and Stuart J Russel, made it simpler to understand than defining it. What they say is that with the combinations of “Thinking like humans” and “Thinking Rationally” one can come to a conclusion about how AI works. They went on to say that

“A computer agent is able to act autonomously, perceive its environment, and persist for an extended period of time, adapt to change, and adopt goals.”

“A rational agent is an agent that is able to act to achieve the expected best outcome (e.g., to persist or reach a goal).”

With the above definitions, it is clear that the computer not only works autonomously but also works with the rational agent which gives the best outcome.

1.1.3 Applications of Artificial Intelligence

Today machines can play brain-demanding games such as Pogo Atari, chess and defeat the champions in the fields. After the Second World War, the changing geopolitical landscape and the available technologies resulted in a revolution in all kinds of industries including healthcare and manufacturing. Large volumes of data started to pour in resulting in the development of machines with large computing power, built with intelligence. Smart algorithms were developed in languages such as Prolog and Lisp to process the data for robots and satellites. The AI systems took their origin there and continued the journey, undergoing metamorphism to reach the Python or Java-driven deep learning algorithms over the cloud supporting the data from the mobile phone or a gaming box. The availability of computing ...

Table des matières

• Cover Page
• Title Page
• Copyright Page
• Foreword
• Dedication Page
• About the Authors
• About the Reviewer
• Acknowledgement
• Preface
• Errata
• Table of Contents
• 1. Introduction
• 2. Supervised Machine Learning
• 3. System Analysis with Machine Learning/Un-Supervised Learning
• 4. Feature Engineering
• 5. Classification, Clustering, Association Rules, and Regression
• 6. Time Series Analysis
• 7. Data Cleanup, Characteristics and Feature Selection
• 8. Ensemble Model Development
• 9. Design with Deep Learning
• 10. Design with Multi Layered Perceptron (MLP)
• 11. Long Short Term Memory Networks
• 12. Autoencoders
• 13. Applications of Machine Learning and Deep Learning
• 14. Emerging and Future Technologies
• Index