About the Author
Jeremy Howard
ISBN 9789355424273
Rs. 4,400
O'Reilly Media
Deep Learning for Coders with Fastai and PyTorch
Who it's for: programmers new to deep learning who prefer hands-on coding
Deep Learning for Coders with Fastai and PyTorch offers a hands-on, code-first introduction to deep learning using the Fastai library and PyTorch framework. Authored by Jeremy Howard and Sylvain Gugger, this book enables programmers to build and deploy practical AI applications, including image classification, natural language processing, recommendation systems, and generative models, without needing advanced mathematical expertise.Why You Should Read? Master practical deep learning concepts through direct coding examples. Build and deploy real-world AI applications efficiently with Fastai and PyTorch. Gain essential skills for model training, optimization, and interpretability. Learn from leading experts in artificial intelligence and machine learning.
Highlights
- Code-first approach: learn by building real applications
- Covers image classification, NLP, recommendation systems, and generative models
- Uses Fastai library and PyTorch framework
- Focuses on practical skills without advanced math
- Authored by Jeremy Howard and Sylvain Gugger
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Sign inQuestions about this book
What is the main approach of this book?
The book uses a hands-on, code-first approach, teaching deep learning through direct coding examples rather than heavy mathematics.
Which frameworks does the book use?
It uses the Fastai library and PyTorch framework.
What applications are covered?
It covers image classification, natural language processing, recommendation systems, and generative models.
Do I need advanced math knowledge?
No, the book is designed for programmers without advanced mathematical expertise.
Who are the authors?
The authors are Jeremy Howard and Sylvain Gugger, both leading experts in AI and machine learning.