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Total Size:
84.4 MB
Info Hash:
AC04BF3FBFC18C354413A3F06923743D5F8BAADA
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Added:
July 2, 2025, 10:02 a.m.
Stats:
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(Last updated: July 2, 2025, 10:04 a.m.)
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| ['Code.zip'] | 0 bytes |
| ["Moroney L. AI and ML for Coders in Pytorch. A Coder's Guide...2025.pdf"] | 0 bytes |
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84.4 MB
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| Uploaded by andryold1 | Size 84.4 MB | Health [ 21 /17 ] | Added 2025-07-02 |
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| Uploaded by nomdeguerre | Size 526.0 MB | Health [ 19 /7 ] | Added 2023-07-01 |
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| Uploaded by eXpOrTeRICV | Size 400.9 MB | Health [ 0 /3 ] | Added 2023-10-25 |
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2026-03-01
| Uploaded by Anonymous | Size 118.8 MB | Health [ 34 /14 ] | Added 2026-03-01 |
NOTE
SOURCE: Moroney L. AI and ML for Coders in Pytorch. A Coder's Guide...2025
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COVER

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MEDIAINFO
Textbook in PDF format Eager to learn AI and Machine Learning but unsure where to start? Laurence Moroney's hands-on, code-first guide demystifies complex AI concepts without relying on advanced mathematics. Designed for programmers, it focuses on practical applications using PyTorch, helping you build real-world models without feeling overwhelmed. From computer vision and natural language processing (NLP) to Generative AI with Hugging Face Transformers, this book equips you with the skills most in demand for AI development today. You'll also learn how to deploy your models across the web and cloud confidently. The goal of this book is to prepare you, as a coder, for just that—it’s accessible enough if you don’t fully understand ML yet, and also exposes you to the advanced concepts that will help you go deeper. The aim: to equip you to be an ML and AI developer without needing a PhD! Gain the confidence to apply AI without needing advanced math or theory expertise Discover how to build AI models for computer vision, NLP, and sequence modeling with PyTorch Learn generative AI techniques with Hugging Face Diffusers and Transformers Who Should Read This Book: If you’re interested in AI and ML, and you want to get up and running quickly with building models that learn from data, this book is for you. If you’re interested in getting started with common AI and ML concepts—computer vision, natural language processing, sequence modeling, and more—and want to see how neural networks can be trained to solve problems in these spaces, I think you’ll enjoy this book. And if you’ve heard all of the hoopla around generative AI, we roll our sleeves up and explore how that works with transformer and diffuser-based models. Most of all, if you’ve put off entering this valuable area of computer science because of perceived difficulty, in particular believing that you’ll need to dust off your old calculus books, then fear not: this book takes a code-first approach that shows you just how easy it is to get started in the world of ML and artificial intelligence using PyTorch
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