Item #:
104271379
Deep Learning (Adaptive Computation and Machine Learning series)
87% of respondents would recommend this to a friend
Item #:
104271379
MUR 5733
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What Stands Out
Comprehensive Coverage
This book provides an in-depth exploration of deep learning concepts, catering to both beginners and advanced practitioners, ensuring a thorough understanding of the subject matter.
Cutting-Edge Techniques
It includes the latest advancements in algorithms and methodologies, equipping readers with the tools to tackle real-world challenges and stay relevant in a rapidly evolving field.
Practical Applications
The text emphasizes practical use cases, helping readers connect theory with implementation, which enhances learning and encourages innovation in solving complex problems using deep learning.
Product Details
- An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
| Publisher | The MIT Press |
| Publication date | 18 Nov. 2016 |
| Language | English |
| Print length | 800 pages |
| ISBN-10 | 0262035618 |
| ISBN-13 | 978-0262035613 |
| Reading age | 18 years and up |
| Dimensions | 23.11 x 18.29 x 2.79 cm |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for those looking to build foundational knowledge in deep learning and its applications in data science.
-
Researchers in AI
Beneficial for advanced researchers seeking comprehensive insights and techniques in deep learning methodologies.
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Engineering Students
Suitable for engineering students focusing on artificial intelligence and machine learning technologies in their studies.
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Casual Learners
Not suitable for casual readers without a technical background who seek an overview rather than in-depth knowledge.
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Editorial Review
**** The book "Deep Learning," part of the Adaptive Computation and Machine Learning series, has garnered attention for its comprehensive treatment of the subject despite some criticisms regarding its mathematical rigor. The authors, recognized as leading figures in the field, offer insights that blend practical applications with the theoretical underpinnings of deep learning. Reviewers appreciate the emphasis on real-world applicability, noting that the book steers clear of excessive mathematical abstractions that often obscurate practical understanding. Instead, it focuses on actionable insights crucial for practitioners in areas like image and speech recognition. Readers have highlighted the extensive and relevant literature provided, which enhances the reader's ability to delve deeper into specific topics of interest without necessitating prior comprehension of all referenced materials. However, while the book excels in providing a robust overview and practical advice—especially beneficial for both novices and experienced practitioners—it has been noted that some sections may lack the in-depth theoretical formulations expected in a scientific text. Some customers indicate that while the book is an excellent resource for those looking to understand deep learning concepts deeply, it may not fully equip one for the complexities found in advanced academic literature. Moreover, a solid foundation in mathematics, particularly linear algebra and numerical calculus, is highly suggested for optimal comprehension. Overall, the book receives high praise for its clarity, depth, and practical orientation, making it a valuable addition for anyone keen on mastering deep learning techniques. **
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Pros
- Comprehensive and up-to-date overview of deep learning.
- Strong emphasis on practical applications over purely theoretical concepts.
- Valuable insights from leading experts in the field.
- Extensive literature for further study is included.
- Suitable for both beginners and experienced practitioners.
Cons
- Some chapters may lack solid mathematical formulation.
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