Machine Learning with PyTorch and Scikit-Learn. (Record no. 15876)
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| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250710182903.0 |
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| 024 80 - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 9781801816380 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | PACKT |
| Transcribing agency | PACKT |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | en |
| 044 ## - COUNTRY OF PUBLISHING/PRODUCING ENTITY CODE | |
| MARC country code | GB |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Sebastian Raschka |
| Relator term | author. |
| 245 00 - TITLE STATEMENT | |
| Title | Machine Learning with PyTorch and Scikit-Learn. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | GB: |
| Name of publisher, distributor, etc. | Packt, |
| Date of publication, distribution, etc. | 2022-02-25. |
| 263 ## - PROJECTED PUBLICATION DATE | |
| Projected publication date | 2022-02-25 |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | GB: |
| Name of producer, publisher, distributor, manufacturer | Packt, |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 774. |
| 377 ## - ASSOCIATED LANGUAGE | |
| Language code | en |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <p><b>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. Purchase of the print or Kindle book includes a free eBook in PDF format.</b></p><h4>Key Features</h4><ul><li>Learn applied machine learning with a solid foundation in theory</li><li>Clear, intuitive explanations take you deep into the theory and practice of Python machine learning</li><li>Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices</li></ul><h4>Book Description</h4>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.<h4>What you will learn</h4><ul><li>Explore frameworks, models, and techniques for machines to learn from data</li><li>Use scikit-learn for machine learning and PyTorch for deep learning</li><li>Train machine learning classifiers on images, text, and more</li><li>Build and train neural networks, transformers, and boosting algorithms</li><li>Discover best practices for evaluating and tuning models</li><li>Predict continuous target outcomes using regression analysis</li><li>Dig deeper into textual and social media data using sentiment analysis</li></ul><h4>Who this book is for</h4>If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Data in extended ASCII character set. |
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| System details note | Mode of access: Internet. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Yuxi (Hayden) Liu |
| Relator term | author. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Vahid Mirjalili |
| Relator term | author. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Dmytro Dzhulgakov |
| Relator term | author. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | PACKT |
| 773 0# - HOST ITEM ENTRY | |
| Title | Machine Learning with PyTorch and Scikit-Learn |
| Place, publisher, and date of publication | GB,Packt,2022-02-25 |
| Physical description | 774 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://learning.packt.com/product/425761">https://learning.packt.com/product/425761</a> |
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