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Nº de artículo:
13422615
Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
89% of respondents would recommend this to a friend
Nº de artículo:
13422615
USD 36
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What Stands Out
Comprehensive Coverage
This tutorial offers an extensive examination of deep learning mathematics, making complex concepts accessible for beginners while providing in-depth insights for seasoned practitioners looking to enhance their knowledge.
Hands-On Approach
With practical examples and exercises, the book enables readers to apply mathematical concepts in real-world scenarios, enhancing their understanding of artificial intelligence and reinforcing learning through practice.
Expert Insights
Authored by industry professionals, this guide presents cutting-edge techniques and best practices in deep learning, equipping readers with essential skills to tackle current challenges in artificial intelligence development.
Detalles de producto
- authoritative, funny, and concise Steven Strogatz, Professor of Applied Mathematics, Cornell University.The brain has always had a fundamental advantage over conventional computers: it can learn. However, a new generation of artificial intelligence algorithms, in the form of deep neural networks, is rapidly eliminating that advantage. Deep neural networks rely on adaptive algorithms to master a wide variety of tasks, including cancer diagnosis, object recognition, speech recognition, robotic control, chess, poker, backgammon and Go, at super-human levels of performance. In this richly illustrated book, key neural network learning algorithms are explained informally first, followed by detailed mathematical analyses. Topics include both historically important neural networks (perceptrons, Hopfield nets, Boltzmann machines and backpropagation networks), and modern deep neural networks (variational autoencoders, convolutional networks, generative adversarial networks, and reinforcement learning using SARSA and Q-learning). Online computer programs, collated from open source repositories, give hands-on experience of neural networks, and PowerPoint slides provide support for teaching. Written in an informal style, with a comprehensive glossary, tutorial appendices (e.g. Bayes' theorem, maximum likelihood estimation), and a list of further readings, this is an ideal introduction to the algorithmic engines of modern artificial intelligence.Dr James V Stone is an Honorary Associate Professor at the University of Sheffield, England.
| Publisher | Sebtel Press |
| Publication date | March 28, 2019 |
| Language | English |
| Print length | 216 pages |
| ISBN-10 | 0956372813 |
| ISBN-13 | 978-0956372819 |
| Item Weight | 11.4 ounces (323.19 grams) |
| Dimensions | 6 x 0.49 x 9 inches (15.2 x 1.2 x 22.9 cm) |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for those looking to build a foundational understanding of deep learning mathematics to enhance their analytical skills.
-
Computer Science Students
Useful for students wanting to supplement their curriculum with practical insights into deep learning applications and theories.
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AI Practitioners
Benefits professionals needing a refresher in the mathematical concepts crucial for developing and implementing AI models effectively.
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Complete Beginners
Not suitable for individuals with no prior knowledge of mathematics or deep learning, as it assumes some foundational concepts.
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Intelligence & Semantics Editorial Review
Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning offers a foundational insight into deep learning mathematics. Readers have noted that this book is particularly well-suited for those with a basic math background who are newcomers to AI, helping them build a foundation for more complex material. However, some readers experienced confusion with mathematical terms and subscripts, especially in discussions of algorithms like backpropagation. Despite the challenges, many found the writing style engaging and appreciated the humor intertwined within technical explanations, making this book a unique tool for understanding AI concepts on a high level while still requiring further study for in-depth knowledge.
Customer Reviews & Ratings
67 valoraciones de los clientes
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5 estrella
63%
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4 estrella
22%
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3 estrella
10%
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2 estrella
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ventajas
- Engaging writing style with humor
- Great for beginners with some math background
- Covers essential deep learning concepts
- Readable and clear explanations
- Helps build a foundation for advanced studies
Contras
- Some concepts lack adequate definitions making understanding challenging
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