finelybook
Artificial Intelligence By Example Second Edition 版本: Acquire advanced AI,machine learning,and deep learning design skills
by 作者: Denis Rothman
pages 页数: 540 pages
Publisher Finelybook 出版社: Packt Publishing - ebooks Account (March 10,2020)
Language 语言: English
ISBN-10 书号: 1839211539
ISBN-13 书号: 9781839211539
Book Description
Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples.
Artificial intelligence (AI) has the potential to replicate humans in every field. Artificial Intelligence by 作者: Example 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 a 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 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 deep learning and genetic algorithms,conversational user interfaces (CUI) for chatbots,neuromorphic computing,brain-computer interface (BCI) and genetic algorithms.
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 KNN to language translations and explore the opportunities in Google Translate
Chained algorithms combining unsupervised learning with decision trees
Solve the XOR problem with FNN (Feedforward Neural Networks) 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 RNN & LSTM
Building Conversational User Interfaces(CUI) for Chatbots
Writing genetic algorithms that optimize deep learning neural networks
Building Quantum computing circuits
Contents
Preface
Chapter 1: Getting Started with Next-Generation Artificial Intelligence through
Reinforcement Learning
Chapter 2: Building a Reward Matrix-Designing Your Datasets
Chapter 3: Machine Intelligence-Evaluation Functions and Numerical Convergence
Chapter 4: Optimizing Your Solutions with K-Means Clustering
Chapter 5: How to Use Decision Trees to Enhance K-Means Clustering
Chapter 6: Innovating Al with Google Translate
Chapter 7: Optimizing Blockchains with Naive Bayes
Chapter 8: Solving the XOR Problem with a Feedforward Neural Network
Chapter 9: Abstract Image Classification with Convolutional Neural Networks(CNNs)
Chapter 10: Conceptual Representation Learning
Chapter 11: Combining Reinforcement Learning and Deep Learning
Chapter 12: Al and the Internet of Things(loT)
Chapter 13: Visualizing Networks with TensorFlow 2.x and TensorBoard
Chapter 14: Preparing the Input of Chatbots with Restricted Boltzmann Machines(RBMs)
and Principal Component Analysis (PCA)
Chapter 15: Setting Up a Cognitive NLP UI/CUI Chatbot
Chapter 16: Improve the Emotional Intelligence Deficiencies of Chatbots
Chapter 17: Genetic Algorithms in Hybrid Neural Networks
Chapter 18: Neuromorphic Computing
Chapter 19: Quantum Computing
Appendix: Answers to the Questions
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Index
