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| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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230205b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781801819312 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
RAS |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Raschka, Sebastian |
| 245 ## - TITLE STATEMENT |
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| Title |
Machine learning with PyTorch and Scikit-Learn : develop machine learning and deep learning models with Python |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Birmingham : |
| Name of publisher, distributor, etc |
Packt Publishing |
| Date of publication, distribution, etc |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxviii, 741p.; |
| Other physical details |
ill. |
| Dimensions |
24 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
3899.00 |
| Price type code |
INR |
| Unit of pricing |
1.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book Description Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn Explore frameworks, models, and techniques for machines to 'learn' from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you know some Python and you want to use machine learning and deep learning, pick up this book. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Apprentissage automatique |
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| Topical term or geographic name as entry element |
Data Mining |
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| Topical term or geographic name as entry element |
Machine learning |
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| Topical term or geographic name as entry element |
Python |
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Computer program language |
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| Topical term or geographic name as entry element |
Attention weights |
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Breast cancer wisconsin dataset |
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| Topical term or geographic name as entry element |
Car's fuel efficiency prediction project |
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Convolutional neural network(CNNs) |
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Deep learning |
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| Topical term or geographic name as entry element |
Ensemble methods |
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| Topical term or geographic name as entry element |
Feature transformation |
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| Topical term or geographic name as entry element |
Generative model |
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| Topical term or geographic name as entry element |
Holdout method |
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| Topical term or geographic name as entry element |
Iris dataset |
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| Topical term or geographic name as entry element |
McCulloch-Pitts neuron model |
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| Topical term or geographic name as entry element |
No free lunch theorem |
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| Topical term or geographic name as entry element |
Output spectrum |
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| Topical term or geographic name as entry element |
Predictive model |
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| Topical term or geographic name as entry element |
RANdom sample-consensus (RANSAC |
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| Topical term or geographic name as entry element |
Sigmoid function |
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| Topical term or geographic name as entry element |
Weight decays |
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| Topical term or geographic name as entry element |
XG Boost |
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| Topical term or geographic name as entry element |
Zerio-shot tasks |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Liu, Yuxi |
|
|---|
| Personal name |
Mirjalili, Vahid |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Item type |
Books |