Applied Machine Learning with Transformers
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Book 1
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Home > Computing and Information Technology > Computer science > Artificial intelligence > Applied Machine Learning with Transformers: Proven techniques for fine tuning, optimizing, and scaling transformer models for real projects.
Applied Machine Learning with Transformers: Proven techniques for fine tuning, optimizing, and scaling transformer models for real projects.

Applied Machine Learning with Transformers: Proven techniques for fine tuning, optimizing, and scaling transformer models for real projects.


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About the Book

Master applied machine learning with transformers through real projects, working code, and strategies you can use right away. Transformers are at the center of today's advances in natural language processing, computer vision, and generative AI. But moving from research papers to reliable, production-ready systems is still a challenge for many practitioners. This book shows you how to bridge that gap, combining clear explanations, tested practices, and extensive Python examples that let you experiment as you read. You will learn how to prepare and adapt datasets for training, fine tune large models using both full model and parameter-efficient methods, and apply alignment techniques such as supervised fine tuning, reinforcement learning with human feedback, and direct preference optimization. The book also covers scaling strategies with distributed training and memory optimization, as well as compression approaches like quantization and distillation to keep deployments lightweight and efficient. Because building real applications requires more than theory, every major technique is paired with practical tips, working code, and reference tools that support your workflow. Quick-access aids like a Cheat Sheet, Key Takeaways, and Flashcards reinforce the core concepts, while the Index and Common Mistakes sections make it easy to find solutions and avoid pitfalls when you are working under pressure. With this guide, you will not only understand transformers but also know how to put them into production. Grab your copy today and start applying these methods with confidence.


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Product Details
  • ISBN-13: 9798262995790
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 229 mm
  • No of Pages: 210
  • Returnable: N
  • Sub Title: Proven techniques for fine tuning, optimizing, and scaling transformer models for real projects.
  • Width: 152 mm
  • ISBN-10: 8262995791
  • Publisher Date: 30 Aug 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 11 mm
  • Weight: 340 gr


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