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Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms - Tapa blanda
Sinopsis
We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics.
The updated second edition of this book describes the intuition behind these innovations without jargon or complexity. Python-proficient programmers, software engineering professionals, and computer science majors will be able to reimplement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best developers in the field.
- Learn the mathematics behind machine learning jargon
- Examine the foundations of machine learning and neural networks
- Manage problems that arise as you begin to make networks deeper
- Build neural networks that analyze complex images
- Perform effective dimensionality reduction using autoencoders
- Dive deep into sequence analysis to examine language
- Explore methods in interpreting complex machine learning models
- Gain theoretical and practical knowledge on generative modeling
- Understand the fundamentals of reinforcement learning
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Nithin Buduma is one of the first machine learning engineers at XY.ai, a start-up based out of Harvard and Stanford working to help healthcare companies leverage their massive datasets.
Nikhil Buduma is the cofounder and chief scientist of Remedy, a San Francisco-based company that is building a new system for data-driven primary healthcare. At the age of 16, he managed a drug discovery laboratory at San Jose State University and developed novel low-cost screening methodologies for resource-constrained communities. By the age of 19, he was a two-time gold medalist at the International Biology Olympiad. He later attended MIT, where he focused on developing large-scale data systems to impact healthcare delivery, mental health, and medical research. At MIT, he cofounded Lean On Me, a national nonprofit organization that provides an anonymous text hotline to enable effective peer support on college campus and leverages data to effect positive mental health and wellness outcomes. Today, Nikhil spends his free time investing in hard technology and data companies through his venture fund, Q Venture Partners, and managing a data analytics team for the Milwaukee Brewers baseball team.
Joe Papa has over 25 years experience in research & development and is the founder of INSPIRD.ai. He holds an MSEE and has led AI Research teams with PyTorch at Booz Allen and Perspecta Labs. Joe has mentored hundreds of Data Scientists and has taught 6,000+ students across the world on Udemy.
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Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
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O'Reilly Media (edition 2), 2022
ISBN 10: 149208218X
ISBN 13: 9781492082187
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Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
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O'Reilly Media, 2022
ISBN 10: 149208218X
ISBN 13: 9781492082187
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Fundamentals of Deep Learning
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O'Reilly Media, 2022
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ISBN 13: 9781492082187
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Tapa blanda
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Fundamentals of Deep Learning
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O'Reilly Media, 2022
ISBN 10: 149208218X
ISBN 13: 9781492082187
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Tapa blanda
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Fundamentals of Deep Learning : Designing Next-generation Machine Intelligence Algorithms
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Fundamentals of Deep Learning (Paperback)
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ISBN 13: 9781492082187
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Paperback. Condición: new. Paperback. We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception that has powered our push toward self-driving vehicles, the ability to defeat human experts at a variety of difficult games including Go and Starcraft, and even generate essays with shockingly coherent prose. But deciphering these breakthroughs often takes a Ph.D. education in machine learning and mathematics.This updated second edition describes the intuition behind these innovations without the jargon and complexity. By the end of this book, Python-proficient programmers, software engineering professionals, and computer science majors will be able to re-implement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best in the field.New chapters cover recent advancements in the fields of generative modeling and interpretability. Code examples throughout the book are updated to TensorFlow 2 and PyTorch 1.4. This updated second edition describes the intuition behind deep learning innovations without jargon or complexity. By the end of this book, Python-proficient programmers, software engineering professionals, and computer science majors will be able to re-implement these breakthroughs on their own. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781492082187
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Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms (Paperback or Softback)
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Fundamentals of Deep Learning
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