EBOOK
Artificial Intelligence by Example
Acquire advanced AI, machine learning, and deep learning design skills
Denis Rothman1
(1)
About
Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples
Key Features
AI-based examples to guide you in designing and implementing machine intelligence
Build machine intelligence from scratch using artificial intelligence examples
Develop machine intelligence from scratch using real artificial intelligence
AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples.
This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs).
This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing.
By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions.
What you will learn
Apply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google Translate
Understand chained algorithms combining unsupervised learning with decision trees
Solve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graph
Learn about meta learning models with hybrid neural networks
Create a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data logging
Building conversational user interfaces (CUI) for chatbots
Writing genetic algorithms that optimize deep learning neural networks
Build quantum computing circuits
Who this book is for
Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book.
Key Features
AI-based examples to guide you in designing and implementing machine intelligence
Build machine intelligence from scratch using artificial intelligence examples
Develop machine intelligence from scratch using real artificial intelligence
AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples.
This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs).
This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing.
By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions.
What you will learn
Apply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google Translate
Understand chained algorithms combining unsupervised learning with decision trees
Solve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graph
Learn about meta learning models with hybrid neural networks
Create a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data logging
Building conversational user interfaces (CUI) for chatbots
Writing genetic algorithms that optimize deep learning neural networks
Build quantum computing circuits
Who this book is for
Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book.