Deep learning with PyTorch : (Record no. 141676)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 02320nam a22002897a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260727151005.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260727s2026 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781633438859 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | PK-LaUMT |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.133 |
| Item number | DEE- |
| 245 00 - TITLE STATEMENT | |
| Title | Deep learning with PyTorch : |
| Remainder of title | training and applying deep learning and generative AI models / |
| Statement of responsibility, etc | Luca Antiga . . . [et al.] |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Shelter Island : |
| Name of publisher, distributor, etc | Manning, |
| Date of publication, distribution, etc | 2026 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxviii, 514 p. |
| 500 ## - GENERAL NOTE | |
| General note | Index present |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Everything you need to create neural networks with PyTorch, including Large Language and diffusion models. PyTorch core developer Howard Huang updates the bestselling original Deep Learning with PyTorch with new insights into the transformers architecture and generative AI models. In Deep Learning with PyTorch, Second Edition you⁰́₉ll find: Deep learning fundamentals reinforced with hands-on projects Mastering PyTorch's flexible APIs for neural network development Implementing CNNs, transformers, and diffusion models Optimizing models for training and deployment Generative AI models to create images and text Instantly familiar to anyone who knows PyData tools like NumPy, PyTorch simplifies deep learning without sacrificing advanced features. In Deep Learning with PyTorch, Second Edition you⁰́₉ll learn how to create your own neural network and deep learning systems and take full advantage of PyTorch⁰́₉s built-in tools for automatic differentiation, hardware acceleration, distributed training, and more. You⁰́₉ll discover how easy PyTorch makes it to build your entire DL pipeline, including using the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. Each new technique you learn is put into action with practical code examples in each chapter, culminating into you building your own convolution neural networks, transformers, and even a real-world medical image classifier |
| 546 ## - LANGUAGE NOTE | |
| Language note | Eng |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Python (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | PyTorch (electronic resource) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Deep learning |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Antiga, Luca |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Stevens, Eli |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Huang, Howard |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Viehmann, Thomas |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Home library | Current library | Date acquired | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|
| UMT Main Campus | UMT Main Campus | 2026-07-27 | 005.133 DEE- | 153669 | 2026-07-27 | 2026-07-27 | Books |