Artificial Intelligence By Example
Acquire advanced AI, machine learning, and deep learning design skills, 2nd Edition
Denis Rothman
- 578 pagine
- English
- ePUB (disponibile sull'app)
- Disponibile su iOS e Android
Artificial Intelligence By Example
Acquire advanced AI, machine learning, and deep learning design skills, 2nd Edition
Denis Rothman
Informazioni sul libro
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
Book Description
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 withdecision trees
- Solve the XOR problem with feedforward neural networks (FNN) and buildits architecture to represent a data flow graph
- Learn about meta learning models withhybrid neural networks
- Create a chatbot and optimize its emotional intelligence deficiencies withtools such as Small Talk and data logging
- Building conversational user interfaces (CUI) for chatbots
- Writing genetic algorithms that optimizedeep 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.
Domande frequenti
Informazioni
11
Combining Reinforcement Learning and Deep Learning
- Planning and scheduling today and tomorrow
- Further generalization of the CRLMM described in Chapter 10, Conceptual Representation Learning, applied to an apparel production process
- Feeding the CRLMM convolutional neural network (CNN) with a simulation of frames coming from a webcam on a production line
- Introducing an optimizer that will use weights applied to production stations to input a reward matrix to a Markov decision process (MDP), which will then update the weights
- Building a program that will run continuously (no beginning, no end) on a production line using all the three components mentioned previously
Planning and scheduling today and tomorrow
- Filling a box with clothing
- Trying on the clothing at home
- Returning the clothing if it does not fit
- Purchasing the items that are kept
- First, a box must be chosen.
- Then, there is a delivery period.
- Then, there is a trial period (you cannot try the products forever). During this period, the customer can choose not to purchase anything.
- Finally, the customer confirms the purchase.
Indice dei contenuti
- Preface
- Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning
- Building a Reward Matrix – Designing Your Datasets
- Machine Intelligence – Evaluation Functions and Numerical Convergence
- Optimizing Your Solutions with K-Means Clustering
- How to Use Decision Trees to Enhance K-Means Clustering
- Innovating AI with Google Translate
- Optimizing Blockchains with Naive Bayes
- Solving the XOR Problem with a Feedforward Neural Network
- Abstract Image Classification with Convolutional Neural Networks (CNNs)
- Conceptual Representation Learning
- Combining Reinforcement Learning and Deep Learning
- AI and the Internet of Things (IoT)
- Visualizing Networks with TensorFlow 2.x and TensorBoard
- Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA)
- Setting Up a Cognitive NLP UI/CUI Chatbot
- Improving the Emotional Intelligence Deficiencies of Chatbots
- Genetic Algorithms in Hybrid Neural Networks
- Neuromorphic Computing
- Quantum Computing
- Answers to the Questions
- Other Books You May Enjoy
- Index