Artificial Intelligence By Example (Record no. 46433)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 07500nam a22002057a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250315125248.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250313b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781839211539 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | VITAP |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Edition number | 23rd Ed. |
| Classification number | 006.31 ROT |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Rothman, Denis |
| 9 (RLIN) | 15202 |
| 245 ## - TITLE STATEMENT | |
| Title | Artificial Intelligence By Example |
| Remainder of title | : Acquire advanced AI, machine learning, and deep learning design skills |
| Statement of responsibility, etc. | / Denis Rothman |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd Ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Mumbai |
| Name of publisher, distributor, etc. | Packt Publishing Ltd. |
| Date of publication, distribution, etc. | 2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxi, 549p. : ill. ; 24cm |
| 500 ## - GENERAL NOTE | |
| General note | It includes Index Pages. |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Table of Contents<br/>22 Chapters<br/>Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning Chevron down icon<br/>Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning<br/>Reinforcement learning concepts<br/>How to adapt to machine thinking and become an adaptive thinker<br/>Overcoming real-life issues using the three-step approach<br/>The lessons of reinforcement learning<br/>Summary<br/>Questions<br/>Further reading<br/>Building a Reward Matrix – Designing Your Datasets Chevron down icon<br/>Building a Reward Matrix – Designing Your Datasets<br/>Designing datasets – where the dream stops and the hard work begins<br/>Logistic activation functions and classifiers<br/>Summary<br/>Questions<br/>Further reading<br/>Machine Intelligence – Evaluation Functions and Numerical Convergence Chevron down icon<br/>Machine Intelligence – Evaluation Functions and Numerical Convergence<br/>Tracking down what to measure and deciding how to measure it<br/>Evaluating beyond human analytic capacity<br/>Using supervised learning to evaluate a result that surpasses human analytic capacity<br/>Summary<br/>Questions<br/>Further reading<br/>Optimizing Your Solutions with K-Means Clustering Chevron down icon<br/>Optimizing Your Solutions with K-Means Clustering<br/>Dataset optimization and control<br/>Implementing a k-means clustering solution<br/>Summary<br/>Questions<br/>Further reading<br/>How to Use Decision Trees to Enhance K-Means Clustering Chevron down icon<br/>How to Use Decision Trees to Enhance K-Means Clustering<br/>Unsupervised learning with KMC with large datasets<br/>Summary<br/>Questions<br/>Further reading<br/>Innovating AI with Google Translate Chevron down icon<br/>Innovating AI with Google Translate<br/>Understanding innovation and disruption in AI<br/>Discover a world of opportunities with Google Translate<br/>AI as a new frontier<br/>Summary<br/>Questions<br/>Further reading<br/>Optimizing Blockchains with Naive Bayes Chevron down icon<br/>Optimizing Blockchains with Naive Bayes<br/>Part I – the background to blockchain technology<br/>PART II – using blockchains to share information in a supply chain<br/>Part III – optimizing a supply chain with naive Bayes in a blockchain process<br/>Summary<br/>Questions<br/>Further reading<br/>Solving the XOR Problem with a Feedforward Neural Network Chevron down icon<br/>Solving the XOR Problem with a Feedforward Neural Network<br/>The original perceptron could not solve the XOR function<br/>Building an FNN from scratch<br/>Applying the FNN XOR function to optimizing subsets of data<br/>Summary<br/>Questions<br/>Further reading<br/>Abstract Image Classification with Convolutional Neural Networks (CNNs) Chevron down icon<br/>Abstract Image Classification with Convolutional Neural Networks (CNNs)<br/>Introducing CNNs<br/>Training a CNN model<br/>Summary<br/>Questions<br/>Further reading and references<br/>Conceptual Representation Learning Chevron down icon<br/>Conceptual Representation Learning<br/>Generating profit with transfer learning<br/>Domain learning<br/>Summary<br/>Questions<br/>Further reading<br/>Combining Reinforcement Learning and Deep Learning Chevron down icon<br/>Combining Reinforcement Learning and Deep Learning<br/>Planning and scheduling today and tomorrow<br/>CRLMM applied to an automated apparel manufacturing process<br/>Building the RL-DL-CRLMM<br/>Summary<br/>Questions<br/>Further reading<br/>AI and the Internet of Things (IoT) Chevron down icon<br/>AI and the Internet of Things (IoT)<br/>The public service project<br/>Setting up the RL-DL-CRLMM model<br/>Adding an SVM function<br/>Running the CRLMM<br/>Summary<br/>Questions<br/>Further reading<br/>Visualizing Networks with TensorFlow 2.x and TensorBoard Chevron down icon<br/>Visualizing Networks with TensorFlow 2.x and TensorBoard<br/>Exploring the output of the layers of a CNN in two steps with TensorFlow<br/>Analyzing the accuracy of a CNN using TensorBoard<br/>Summary<br/>Questions<br/>Further reading<br/>Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA) Chevron down icon<br/>Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA)<br/>Defining basic terms and goals<br/>Introducing and building an RBM<br/>Using the weights of an RBM as feature vectors for PCA<br/>Summary<br/>Questions<br/>Further reading<br/>Setting Up a Cognitive NLP UI/CUI Chatbot Chevron down icon<br/>Setting Up a Cognitive NLP UI/CUI Chatbot<br/>Basic concepts<br/>Adding fulfillment functionality to an agent<br/>Machine learning agents<br/>Summary<br/>Questions<br/>Further reading<br/>Improving the Emotional Intelligence Deficiencies of Chatbots Chevron down icon<br/>Improving the Emotional Intelligence Deficiencies of Chatbots<br/>From reacting to emotions, to creating emotions<br/>Data logging<br/>Creating emotions<br/>RNN research for future automatic dialog generation<br/>Summary<br/>Questions<br/>Further reading<br/>Genetic Algorithms in Hybrid Neural Networks Chevron down icon<br/>Genetic Algorithms in Hybrid Neural Networks<br/>Understanding evolutionary algorithms<br/>Artificial hybrid neural networks<br/>Summary<br/>Questions<br/>Further reading<br/>Neuromorphic Computing Chevron down icon<br/>Neuromorphic Computing<br/>Neuromorphic computing<br/>Getting started with Nengo<br/>Applying Nengo's unique approach to critical AI research areas<br/>Summary<br/>Questions<br/>References<br/>Further reading<br/>Quantum Computing Chevron down icon<br/>Quantum Computing<br/>The rising power of quantum computers<br/>A thinking quantum computer<br/>Summary<br/>Questions<br/>Further reading<br/>Answers to the Questions Chevron down icon<br/>Chapter 1 – Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning<br/>Chapter 2 – Building a Reward Matrix – Designing Your Datasets<br/>Chapter 3 – Machine Intelligence – Evaluation Functions and Numerical Convergence<br/>Chapter 4 – Optimizing Your Solutions with K-Means Clustering<br/>Chapter 5 – How to Use Decision Trees to Enhance K-Means Clustering<br/>Chapter 6 – Innovating AI with Google Translate<br/>Chapter 7 – Optimizing Blockchains with Naive Bayes<br/>Chapter 8 – Solving the XOR Problem with a Feedforward Neural Network<br/>Chapter 9 – Abstract Image Classification with Convolutional Neural Networks (CNNs)<br/>Chapter 10 – Conceptual Representation Learning<br/>Chapter 11 – Combining Reinforcement Learning and Deep Learning<br/>Chapter 12 – AI and the Internet of Things<br/>Chapter 13 – Visualizing Networks with TensorFlow 2.x and TensorBoard<br/>Chapter 14 – Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA)<br/>Chapter 15 – Setting Up a Cognitive NLP UI/CUI Chatbot<br/>Chapter 16 – Improving the Emotional Intelligence Deficiencies of Chatbots<br/>Chapter 17 – Genetic Algorithms in Hybrid Neural Networks<br/>Chapter 18 – Neuromorphic Computing<br/>Chapter 19 – Quantum Computing<br/>Other Books You May Enjoy Chevron down icon<br/>Other Books You May Enjoy<br/>Index Chevron down icon<br/>Index<br/> |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning; Artificial Intelligence |
| 9 (RLIN) | 15203 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Reference Book |
| Edition | 23rd |
| Classification part | 006.31 |
| Call number suffix | ROT |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Materials specified (bound volume or other part) | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Inventory number | Total Checkouts | Full call number | Barcode | Date last seen | Cost, replacement price | Price effective from | Koha item type | Public note |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Paper Back | Reference | School of Computer Science Section | VIT-AP | Reference | 2025-03-08 | Shah Book House Pvt. Ltd., Hyderabad | 3299.00 | SBH/27176 | 006.31 ROT | 023051 | 2025-03-13 | 3299.00 | 2025-03-08 | Reference Book | CSE |