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- Practical Deep Learning, 2nd Edition: A Python-Based Introduction, (Paperback)
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Nº de artículo:
155690316
Practical Deep Learning, 2nd Edition: A Python-Based Introduction, (Paperback)
Nº de artículo:
155690316
CLP 68269
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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.
Detalles de producto
- 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).
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | July, 2025 |
| Pages | 584 |
| Reading level | General |
| Subgenre | Data Science |
| Series title | No Series |
| Edition | 1 |
| Publisher | No Starch Press |
| Original languages | English |
| Language | English |
| Edu focus | Technology |
| Educational level | General |
| Is collectible | N |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 7.01 x 1.18 x 9.21 in (17.8 x 3 x 23.4 cm) |
| Assembled product weight | 2.2 lb (1 kg) |
| Bisac subject heading | Computers |
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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CLP 68269
Haz tu pedido ahora y recíbelo por ahí Viernes, Octubre 09
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QTY:
Solo quedan 36 artículos en stock.
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PCI DSS compliant and ISO 27001:2022 certified, with encrypted payments and full buyer protection on every order.
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Fastest cross-border delivery
características y beneficios
- 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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