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Deep Learning (Adaptive Computation And Machine Learning Series)
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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.
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This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: How many pages does the book have? A: This book has eight hundred pages. It provides an in-depth exploration of deep learning topics, making it suitable for both students and professionals.
- Q: What is the binding type of the book? A: The book is available in hardcover. This ensures durability and makes it suitable for frequent use.
- Q: What are the dimensions of the book? A: The book measures nine point one inches in length, seven point two inches in width, and one point one inches in height. These dimensions make it easy to handle and store.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for beginners. It introduces deep learning concepts with mathematical and conceptual background, making it accessible to new learners.
- Q: Can professionals benefit from this book? A: Yes, professionals can benefit from this book. It covers advanced deep learning techniques used in industry, making it a valuable resource for practitioners.
- Q: What level of education is this book designed for? A: This book is designed for both undergraduate and graduate students. It also caters to software engineers looking to implement deep learning in their work.
- Q: How should I care for this book? A: To care for the book, keep it in a dry place away from direct sunlight. Avoid bending the pages and clean it with a soft cloth to maintain its condition.
- Q: Is the book safe for children? A: Yes, the book is safe for children, as it does not contain any inappropriate content. However, it is recommended for older children due to its complex subject matter.
- Q: What is the recommended storage for the book? A: It's best to store the book upright on a shelf. Ensure it is in a cool, dry location to prevent any moisture damage.
- Q: What happens if the book arrives damaged? A: If the book arrives damaged, you should contact the seller for a return or exchange. Most sellers have policies in place to address such issues.
- Q: Can I return the book if I don't like it? A: Yes, you can typically return the book if you don't like it, subject to the seller's return policy. Check the specific terms for return eligibility.
- Q: What if I have trouble understanding the content? A: If you have trouble understanding the content, consider using supplementary materials available on the author's website. They offer additional resources for readers.
- Q: Is the book focused on practical applications? A: Yes, the book focuses on practical applications of deep learning. It discusses techniques used in various fields such as natural language processing and computer vision.
- Q: Does the book cover theoretical concepts? A: Yes, the book covers theoretical concepts in deep learning. It includes topics such as linear factor models and deep generative models, providing a comprehensive overview.
- Q: What is the author’s background? A: The author, Ian Goodfellow, is a recognized expert in deep learning. His expertise adds credibility to the content presented in the book.