Title
Deep Learning (Adaptive Computation And Machine Learning Series)
Delivery time: 8-12 business days (International)
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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Shipping & Returns
Shipping
We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
Returns
To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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Warranty
We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What topics are covered in 'Deep Learning'? A: The book covers a broad range of topics in deep learning, including mathematical and conceptual backgrounds, deep learning techniques used in industry, and research perspectives. It addresses concepts in linear algebra, probability theory, and information theory, as well as applications like natural language processing and computer vision.
- Q: Who are the authors of 'Deep Learning'? A: 'Deep Learning' is authored by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, who are recognized experts in the field of machine learning and artificial intelligence.
- Q: Is 'Deep Learning' suitable for beginners? A: Yes, 'Deep Learning' is suitable for both undergraduate and graduate students, as well as software engineers who are beginning to explore deep learning techniques.
- Q: What is the publication date of 'Deep Learning'? A: 'Deep Learning' was published on November 18, 2016.
- Q: How many pages does 'Deep Learning' have? A: 'Deep Learning' contains a total of 800 pages, providing an in-depth exploration of its topics.
- Q: What type of binding does 'Deep Learning' have? A: 'Deep Learning' is available in hardcover binding, ensuring durability for frequent use.
- Q: Does 'Deep Learning' include practical examples? A: Yes, the book includes practical examples of deep learning techniques used in industry, such as deep feedforward networks, convolutional networks, and more.
- Q: Can 'Deep Learning' be used as a textbook? A: Yes, 'Deep Learning' can be used as a textbook for courses in deep learning, as it offers supplemental material for both readers and instructors.
- Q: What are some applications of deep learning discussed in the book? A: The book discusses various applications of deep learning, including natural language processing, speech recognition, computer vision, and online recommendation systems.
- Q: What is the target audience for 'Deep Learning'? A: The target audience includes undergraduate and graduate students, researchers, and software engineers interested in utilizing deep learning in their work.