| Tag |
First Indicator |
Second Indicator |
Subfields |
| LEADER |
00000nam a2200000 i 4500 |
| 001 |
in00005504313 |
| 006 |
m o d |
| 007 |
cr |n||||||||n |
| 008 |
210416s2021||||nyu|||||o|||||||||||eng|| |
| 005 |
20241008175915.1 |
| 010 |
|
|
|z 2020949936
|
| 020 |
|
|
|a 9781260462302 (e-ISBN)
|
| 020 |
|
|
|a 1260462307 (e-ISBN)
|
| 020 |
|
|
|a 9781260462296 (print-ISBN)
|
| 020 |
|
|
|a 1260462293 (print-ISBN)
|
| 035 |
|
|
|a (OCoLC)1245575418
|
| 035 |
|
|
|a (IN-ChSCO)9781260462302
|
| 040 |
|
|
|a IN-ChSCO
|b eng
|e rda
|
| 041 |
0 |
|
|a eng
|
| 050 |
|
4 |
|a QA76.73.P98
|
| 072 |
|
7 |
|a TEC
|x 007000
|2 bisacsh
|
| 082 |
0 |
4 |
|a 005.133
|2 23
|
| 100 |
1 |
|
|a Kadre, Shailendra,
|e author.
|
| 245 |
1 |
0 |
|a Machine Learning and Deep Learning Using Python and TensorFlow /
|c Shailendra Kadre, Venkata Reddy Konasani.
|
| 250 |
|
|
|a First edition.
|
| 264 |
|
1 |
|a New York, N.Y. :
|b McGraw-Hill Education,
|c [2021]
|
| 264 |
|
4 |
|c 2021
|
| 300 |
|
|
|a 1 online resource (600 pages) :
|b 50 illustrations.
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 490 |
1 |
|
|a McGraw-Hill's AccessEngineering
|
| 504 |
|
|
|a Includes bibliographical references and index.
|
| 505 |
0 |
|
|a Cover -- Title Page -- Copyright Page -- Dedication -- About the Authors -- Contents -- Acknowledgments -- Preface -- Chapter 1. Introduction to Machine Learning and Deep Learning -- 1.1 A Brief History of AI and Machine Learning -- 1.2 Building Blocks of a Machine Learning Project -- 1.3 Machine Learning Algorithms vs. Traditional Computer Programs -- 1.4 How Deep Learning Works -- 1.5 Machine Learning and Deep Learning Applications -- 1.6 The Organization of This Book -- 1.7 Prerequisites?Essential Mathematics -- 1.8 The Terminology You Should Know -- 1.9 Machine Learning?A Wider Outlook Will Certainly Help -- 1.10 Python and Its Potential as the Language of Machine Learning -- 1.11 About TensorFlow -- 1.12 Conclusion -- 1.13 References -- Chapter 2. Basics of Python Programming and Statistics -- 2.1 Introduction to Python -- 2.2 Getting Started with Python Coding -- 2.3 Types of Objects in Python -- 2.4 Python Packages -- 2.5 Conditions and Loops in Python -- 2.6 Data Handling and Pandas Deep Dive -- 2.7 Basic Descriptive Statistics -- 2.8 Data Exploration -- 2.9 Conclusion -- 2.10 Practice Problems -- 2.11 References -- Chapter 3. Regression and Logistic Regression -- 3.1 What Is Regression? -- 3.2 Regression Model Building -- 3.3 R-Squared -- 3.4 Multiple Regression -- 3.5 Multicollinearity in Regression -- 3.6 Individual Impact of the Variables in Regression -- 3.7 Steps Needed in Building a Regression Model -- 3.8 Logistic Regression Model -- 3.9 Logistic Regression Model Building -- 3.10 Accuracy of Logistic Regression Line -- 3.11 Multiple Logistic Regression Line -- 3.12 Multicollinearity in Logistic Regression -- 3.13 Individual Impact of the Variables -- 3.14 Steps in Building a Logistic Regression Model -- 3.15 Linear vs. Logistic Regression Comparison -- 3.16 Conclusion -- 3.17 Practice Problems -- 3.18 Reference -- Chapter 4. Decision Trees -- 4.1 What Are Decision Trees? -- 4.2 Splitting Criterion Metrics: Entropy and Information Gain -- 4.3 Decision Tree Algorithm -- 4.4 Case Study: Contact Center Customer Segmentation -- 4.5 The Problem of Overfitting -- 4.6 Pruning of Decision Trees -- 4.7 The Challenge of Underfitting -- 4.8 Binary Search on Pruning Parameters -- 4.9 More Pruning Parameters -- 4.10 Steps in Building a Decision Tree Model -- 4.11 Conclusion -- 4.12 Practice Problems -- Chapter 5. Model Selection and Cross-Validation -- 5.1 Steps in Building a Model -- 5.2 Model Validation Measures: Regression -- 5.3 Case Study: House Sales in King County, Washington -- 5.4 Model Validation Measures: Classification -- 5.5 Bias-Variance Trade-Off -- 5.6 Cross-Validation -- 5.7 Feature Engineering Tips and Tricks -- 5.8 Dealing with Class Imbalance -- 5.9 Conclusion -- 5.10 Practice Problems -- 5.11 References -- Chapter 6. Cluster Analysis -- 6.1 Unsupervised Learning -- 6.2 Distance Measure -- 6.3 K-Means Clustering Algorithm -- 6.4 Building K-Means Clusters -- 6.5 Deciding the Number of Clusters -- 6.6 Conclusion -- 6.7 Practice Problems -- 6.8 References -- Chapter 7. Random Forests and Boosting -- 7.1 Ensemble Models -- 7.2 Bagging -- 7.3 Random Forest -- 7.4 Case Study: Car Accidents Prediction -- 7.5 Boosting -- 7.6 AdaBoosting Algorithm -- 7.7 Gradient Boosting Algorithm -- 7.8 Case Study: Income Prediction from Census Data -- 7.9 Conclusion -- 7.10 Practice Problems -- 7.11 References -- Chapter 8. Artificial Neural Networks -- 8.1 Network Diagram for Logistic Regression -- 8.2 Concept of Decision Boundary -- 8.3 Multiple Decision Boundaries Problem -- 8.4 Multiple Decision Boundaries Solution -- 8.5 Neural Network Intuition -- 8.6 Neural Network Algorithm -- 8.7 The Concept of Gradient Descent -- 8.8 Case Study: Recognizing Handwritten Digits -- 8.9 Deep Neural Networks -- 8.10 Conclusion -- 8.11 Practice Problems -- 8.12 References -- Chapter 9. TensorFlow and Keras -- 9.1 Deep Neural Networks -- 9.2 Deep Learning Frameworks -- 9.3 Key Terms in TensorFlow -- 9.4 Model Building with TensorFlow -- 9.5 Keras -- 9.6 Conclusion -- 9.7 References -- Chapter 10. Deep Learning Hyperparameters -- 10.1 Regularization -- 10.2 Dropout Regularization -- 10.3 Early Stopping Method -- 10.4 Loss Functions -- 10.5 Activation Functions -- 10.6 Learning Rate -- 10.7 Optimizers -- 10.8 Conclusion -- Chapter 11. Convolutional Neural Networks -- 11.1 ANNs for Images -- 11.2 Filters -- 11.3 The Convolution Layer -- 11.4 Pooling Layer -- 11.5 CNN Architecture -- 11.6 Case Study: Sign Language Reading from Images -- 11.7 Scheming the Ideal CNN Architecture -- 11.8 Steps in Building a CNN Model -- 11.9 Conclusion -- 11.10 Practice Problems -- 11.11 References -- Chapter 12. Recurrent Neural Networks and Long Short-Term Memory -- 12.1 Cross-Sectional Data vs. Sequential Data -- 12.2 Models for Sequential Data -- 12.3 Case Study: Word Prediction -- 12.4 Recurrent Neural Networks -- 12.5 RNN for Long Sequences -- 12.6 Long Short-Term Memory -- 12.7 Sequence to Sequence Models -- 12.8 Case Study: Language Translation -- 12.9 Conclusion -- 12.10 Practice Problems -- 12.11 References -- Index.
|
| 520 |
0 |
|
|a This book provides you with an in-depth treatment of some advanced machine learning methods such as random forests, boosting, and neural networks.
|
| 530 |
|
|
|a Also available in print edition.
|
| 533 |
|
|
|a Electronic reproduction.
|b New York, N.Y. :
|c McGraw Hill,
|d 2021.
|n Mode of access: World Wide Web.
|n System requirements: Web browser.
|n Access may be restricted to users at subscribing institutions.
|
| 538 |
|
|
|a Mode of access: Internet via World Wide Web.
|
| 546 |
|
|
|a In English.
|
| 588 |
|
|
|a Description based on e-Publication PDF.
|
| 650 |
|
0 |
|a Machine learning.
|
| 655 |
|
7 |
|a Electronic books.
|2 local
|
| 700 |
1 |
|
|a Reddy Konasani, Venkata,
|e author.
|
| 710 |
2 |
|
|a McGraw-Hill Companies.
|
| 776 |
0 |
|
|i Print version:
|t Machine Learning and Deep Learning Using Python and TensorFlow.
|b First edition.
|d New York, N.Y. : McGraw-Hill Education, 2021
|w (OCoLC)1245422280
|
| 830 |
|
0 |
|a McGraw-Hill's AccessEngineering.
|
| 856 |
4 |
0 |
|u http://proxy.library.tamu.edu/login?url=https://www.accessengineeringlibrary.com/content/book/9781260462296
|z Connect to the full text of this electronic book
|t 0
|
| 949 |
|
|
|a QA 76.73.P98
|w LC
|c 1
|i 210323-1001
|d 03/23/2021
|l MAIN
|m MGH
|q 1
|r Y
|s YWEB
|u 03/23/2021
|x ENG
|
| 999 |
f |
f |
|s 193968d7-d040-4f4f-8f7d-97623e976748
|i ed63aaca-c127-42eb-931e-27e1a6848d3b
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|d Available Online
|t 0
|e QA76.73.P98
|h Library of Congress classification
|
| 998 |
f |
f |
|a QA76.73.P98
|t 0
|l Available Online
|