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155690316
Practical Deep Learning, 2nd Edition: A Python-Based Introduction, (Paperback)
Item #:
155690316
€ 59
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What Stands Out
Hands-On Approach
This edition emphasizes practical applications, enabling readers to swiftly apply deep learning concepts with real-world Python projects, enhancing learning and retention.
Comprehensive Coverage
The book offers thorough coverage of deep learning techniques, from basics to advanced topics, making it suitable for both beginners and experienced practitioners seeking to deepen their knowledge.
Up-to-Date Content
Updated with the latest trends and tools in deep learning, ensuring readers are equipped with relevant knowledge and skills that align with current industry standards and practices.
Product Details
- Deep learning made simple. Dip into deep learning without drowning in theory with this fully updated edition of Practical Deep Learning from experienced author and AI expert Ronald T. Kneusel. After a brief review of basic math and coding principles, you'll dive into hands-on experiments and learn to build working models for everything from image analysis to creative writing, and gain a thorough understanding of how each technique works under the hood. Whether you're a developer looking to add AI to your toolkit or a student seeking practical machine learning skills, this book will teach you: How neural networks work and how they're trainedHow to use classical machine learning modelsHow to develop a deep learning model from scratchHow to evaluate models with industry-standard metricsHow to create your own generative AI modelsEach chapter emphasizes practical skill development and experimentation, building to a case study that incorporates everything you've learned to classify audio recordings. Examples of working code you can easily run and modify are provided, and all code is freely available on GitHub. With Practical Deep Learning, second edition, you'll gain the skills and confidence you need to build real AI systems that solve real problems. New to this edition: Material on computer vision, fine-tuning and transfer learning, localization, self-supervised learning, generative AI for novel image creation, and large language models for in-context learning, semantic search, and retrieval-augmented generation (RAG).
| Publication date | July, 2025 |
| Author | Ronald T Kneusel |
| Book format | Paperback |
| Genre | Computing & Internet |
| Pages | 584 |
| Age group | Adult |
| Series title | No Series |
| Fiction/nonfiction | Non-Fiction |
| Publisher | No Starch Press |
| Educational level | General |
| Reading level | General |
| Subgenre | Data Science |
| Original languages | English |
| Edu focus | Technology |
| Recording time | 0 min |
| Is collectible | N |
| Bisac subject heading | Computers |
| Edition | 1 |
| Language | English |
| Pub date | 2025-07-08 |
| Item Weight | 2 lbs (910 grams) |
Who Should Buy?
-
Beginners in AI
Perfect for newcomers to artificial intelligence who seek a practical, hands-on learning experience with Python.
-
Data Scientists
Data professionals looking to expand their skill set with deep learning concepts and practical implementations using Python.
-
Educators and Students
Great resource for teachers and learners in academic settings who want structured content on deep learning basics.
-
Advanced Practitioners
Not suited for those with extensive experience in deep learning, as content may be too basic for them.
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Ronald T Kneusel All Books Editorial Review
Practical Deep Learning, 2nd Edition: A Python-Based Introduction, is a comprehensive paperback guide designed for those interested in data science and deep learning. With 584 pages, this non-fiction work published by No Starch Press provides a thorough exploration of technology and computing, catering to a general reading level. The book’s dimensions make it a convenient choice for both home and travel use. Readers have appreciated its clear explanations and practical approach to Python programming in the context of deep learning. The detailed examples help bridge theory and practice, making it accessible to learners and practitioners alike.
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Pros
- Comprehensive guide on deep learning concepts
- Clear explanations for beginners and practitioners
- Practical examples for hands-on learning
- Suitable for general reading level
- Well-structured content for better understanding
Cons
- Heavy for extended reading sessions
Product Price History
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€ 59
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Features & Benefits
- Learn deep learning concepts without extensive theory.
- Hands-on experiments in real-world AI applications.
- Build models for image analysis and creative writing.
- Includes evaluation techniques with industry-standard metrics.
- Updated content on generative AI and recent advancements.
- Accessible code examples available on GitHub for easy modification.
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