MARC details
| 000 -LEADER |
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| fixed length control field |
nam a22 7a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
34739 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
BD-DhAAL |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20211111110434.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
190618t2017 xxka|||| |||| 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781786464392 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
BD-DhAAL |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.3 |
| Edition number |
23 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Joshi, Prateek |
| 9 (RLIN) |
32638 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Artificial intelligence with Python : |
| Remainder of title |
build real-world artifical intelligence applications with python to intelligently interact with the world around you / |
| Statement of responsibility, etc |
Prateek Joshi |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Birmingham, UK : |
| Name of publisher, distributor, etc |
Packt Publishing Ltd., |
| Date of publication, distribution, etc |
2017 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
vi, 430 pages : |
| Other physical details |
illustrations, Diagramme ; |
| Dimensions |
23 cm. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Preface; Chapter 1: Introduction to Artificial Intelligence ; What is Artificial Intelligence?; Why do we need to study AI?; Applications of AI; Branches of AI; Defining intelligence using Turing Test; Making machines think like humans; Building rational agents; General Problem Solver; Solving a problem with GPS; Building an intelligent agent; Types of models; Installing Python 3; Installing on Ubuntu; Installing on Mac OS X; Installing on Windows; Installing packages; Loading data; Summary --<br/>Chapter 2 : Classification and Regression Using Supervised Learning; Supervised versus unsupervised learning; What is classification?; Preprocessing data; Binarization; Mean removal; Scaling; Normalization; Label encoding; Logistic Regression classifier; Naïve Bayes classifier; Confusion matrix; Support Vector Machines; Classifying income data using Support Vector Machines; What is Regression?; Building a single variable regressor; Building a multivariable regressor; Estimating housing prices using a Support Vector Regressor; Summary --<br/>Chapter 3: Predictive Analytics with Ensemble Learning What is Ensemble Learning?; Building learning models with Ensemble Learning; What are Decision Trees?; Building a Decision Tree classifier; What are Random Forests and Extremely Random Forests?; Building Random Forest and Extremely Random Forest classifiers; Estimating the confidence measure of the predictions; Dealing with class imbalance; Finding optimal training parameters using grid search; Computing relative feature importance; Predicting traffic using Extremely Random Forest regressor; Summary --<br/>Chapter 4: Detecting Patterns with Unsupervised Learning What is unsupervised learning?; Clustering data with K-Means algorithm; Estimating the number of clusters with Mean Shift algorithm; Estimating the quality of clustering with silhouette scores; What are Gaussian Mixture Models?; Building a classifier based on Gaussian Mixture Models; Finding subgroups in stock market using Affinity Propagation model; Segmenting the market based on shopping patterns; Summary --<br/>Chapter 5: Building Recommender Systems ; Creating a training pipeline; Extracting the nearest neighbors; Building a K-Nearest Neighbors classifierComputing similarity scores; Finding similar users using collaborative filtering; Building a movie recommendation system; Summary --<br/>Chapter 6: Logic Programming ; What is logic programming?; Understanding the building blocks of logic programming; Solving problems using logic programming; Installing Python packages; Matching mathematical expressions; Validating primes; Parsing a family tree; Analyzing geography; Building a puzzle solver; Summary --<br/>Chapter 7: Heuristic Search Techniques ; What is heuristic search?; Constraint Satisfaction Problems; Local search techniques; Constructing a string using greedy search; Solving a problem with constraints; Solving the region-coloring problem; Building an 8-puzzle solver; Building a maze solver; Summary --<br/>Chapter 8: Genetic Algorithms ; Understanding evolutionary and genetic algorithms; Fundamental concepts in genetic algorithms; Generating a bit pattern with predefined parameters; Visualizing the evolution; Solving the symbol regression problem; Building an intelligent robot controller; Summary --<br/>Chapter 9: Building Games with Artificial Intelligence ; Using search algorithms in games; Combinatorial search; Minimax algorithm; Alpha-Beta pruning; Negamax algorithm; Installing easyAI library; Building a bot to play Last Coin Standing; Building a bot to play Tic-Tac-Toe; Building two bots to play Connect Four™ against each other; Building two bots to play Hexapawn against each other; Summary --<br/>Chapter 10: Natural Language Processing ; Introduction and installation of packages; Tokenizing text data; Converting words to their base forms using stemming; Converting words to their base forms using lemmatization; Dividing text data into chunks; Extracting the frequency of terms using a Bag of Words model; Building a category predictor; Constructing a gender identifier; Building a sentiment analyzer; Topic modeling using Latent Dirichlet Allocation; Summary --<br/>Chapter 11: Probabilistic Reasoning for Sequential Data ; Understanding sequential data; Handling time-series data with Pandas; Slicing time-series data; Operating on time-series data; Extracting statistics from time-series data; Generating data using Hidden Markov Models; Identifying alphabet sequences with Conditional Random Fields; Stock market analysis; Summary --<br/>Chapter 12: Building a Speech Recognizer ; Working with speech signals; Visualizing audio signals; Transforming audio signals to the frequency domain; Generating audio signals; Synthesizing tones to generate music; Extracting speech features; Recognizing spoken words; Summary --<br/>Chapter 13: Object Detection and Tracking ; Installing OpenCV; Frame differencing; Tracking objects using colorspaces; Object tracking using background subtraction; Building an interactive object tracker using the CAMShift algorithm; Optical flow based tracking; Face detection and tracking; Using Haar cascades for object detection; Using integral images for feature extraction; Eye detection and tracking; Summary --<br/>Chapter 14: Artificial Neural Networks ; Introduction to artificial neural networks; Building a neural network; training a neural network; Building a Perceptron based classifier; Constructing a single layer neural network; Constructing a multilayer neural network; Building a vector quantizer; Analyzing sequential data using recurrent neural networks; Visualizing characters in an Optical Character Recognition database; Building an Optical Character Recognition engine; Summary --<br/>Chapter 15: Reinforcement Learning ; Understanding the premise; Reinforcement learning versus supervised learning; Real world examples of reinforcement learning; Building blocks of reinforcement learning; Creating an environment; Building a learning agent; Summary --<br/>Chapter 16: Deep Learning with Convolutional Neural Networks ; What are Convolutional Neural Networks?; Architecture of CNNs; Types of layers in a CNN; Building a perceptron-based linear regressor; Building an image classifier using a single layer neural network; Building an image classifier using a Convolutional Neural Network; Summary. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
<br/>Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! What You Will Learn: Realize different classification and regression techniques; Understand the concept of clustering and how to use it to automatically segment data; See how to build an intelligent recommender system; Understand logic programming and how to use it; Build automatic speech recognition systems; Understand the basics of heuristic search and genetic programming; Develop games using Artificial Intelligence; Learn how reinforcement learning works; Discover how to build intelligent applications centered on images, text, and time series data; See how to use deep learning algorithms and build applications based on it--Publisher website. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
|---|
| Program name |
CSE |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Artificial intelligence -- Data processing. |
| 9 (RLIN) |
32639 |
|
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
| 9 (RLIN) |
32655 |
|
|---|
| Topical term or geographic name as entry element |
Application software--Development |
| 9 (RLIN) |
32656 |
|
|---|
| Topical term or geographic name as entry element |
Computer science |
| 9 (RLIN) |
42538 |
| 852 ## - LOCATION/CALL NUMBER |
|---|
| Location |
Ayesha Abed Library |
| Shelving location |
General Stacks |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Item type |
Book |
