"If you want to learn some of the deeper explanations of deep learning and PyTorch then read this book!" - Tiklu Ganguly
Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems.
In Inside Deep Learning, you will learn how to:
Implement deep learning with PyTorch
Select the right deep learning components
Train and evaluate a deep learning model
Fine tune deep learning models to maximize performance
Understand deep learning terminology
Adapt existing PyTorch code to solve new problems
Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped―you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English.
about the technology
Deep learning isn't just for big tech companies and academics. Anyone who needs to find meaningful insights and patterns in their data can benefit from these practical techniques! The unique ability for your systems to learn by example makes deep learning widely applicable across industries and use-cases, from filtering out spam to driving cars.
about the book
Inside Deep Learning is a fast-paced beginners' guide to solving common technical problems with deep learning. Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theory. You'll learn how deep learning works through plain language, annotated code and equations as you work through dozens of instantly useful PyTorch examples.
As you go, you'll build a French-English translator that works on the same principles as professional machine translation and discover cutting-edge techniques just emerging from the latest research. Best of all, every deep learning solution in this book can run in less than fifteen minutes using free GPU hardware!
about the reader
For Python programmers with basic machine learning skills.
about the author
Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. His research includes deep learning, malware detection, reproducibility in ML, fairness/bias, and high performance computing. He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department. Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. His research includes deep learning, malware detection, reproducibility in ML, fairness/bias, and high performance computing. He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department. Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences.
Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. Inside Deep Learning is a fast-paced beginner's guide to solving common technical problems with deep learning.
Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theory in Inside Deep Learning. You'll learn how deep learning works through plain language, annotated code, and equations as you work through dozens of instantly useful PyTorch examples. As you go, you'll build a French-English translator that works on the same principles as professional machine translation, and discover cutting-edge techniques just emerging from the latest research. Best of all, every deep learning solution in this book can run in less than fifteen minutes using free GPU hardware!
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Anbieter: BooksRun, Philadelphia, PA, USA
Paperback. Zustand: Very Good. Annotated. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting. Bestandsnummer des Verkäufers 1617298638-8-1
Anzahl: 2 verfügbar
Anbieter: -OnTimeBooks-, Phoenix, AZ, USA
Zustand: very_good. Gently read. May have name of previous ownership, or ex-library edition. Binding tight; spine straight and smooth, with no creasing; covers clean and crisp. Minimal signs of handling or shelving. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships USPS Media Mail. Bestandsnummer des Verkäufers OTV.1617298638.VG
Anzahl: 1 verfügbar
Anbieter: Greenworld Books, Arlington, TX, USA
Zustand: good. Fast Free Shipping â" Good condition. It may show normal signs of use, such as light writing, highlighting, or library markings, but all pages are intact and the book is fully readable. A solid, complete copy that's ready to enjoy. Bestandsnummer des Verkäufers GWV.1617298638.G
Anzahl: 1 verfügbar
Anbieter: HPB-Red, Dallas, TX, USA
paperback. Zustand: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! Bestandsnummer des Verkäufers S_456455459
Anzahl: 1 verfügbar
Anbieter: INDOO, Avenel, NJ, USA
Zustand: As New. Unread copy in mint condition. Bestandsnummer des Verkäufers SS9781617298639
Anzahl: Mehr als 20 verfügbar
Anbieter: INDOO, Avenel, NJ, USA
Zustand: New. Brand New. Bestandsnummer des Verkäufers 9781617298639
Anzahl: Mehr als 20 verfügbar
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: New. Bestandsnummer des Verkäufers 43037542-n
Anzahl: Mehr als 20 verfügbar
Anbieter: Rarewaves USA, OSWEGO, IL, USA
Paperback. Zustand: New. "If you want to learn some of the deeper explanations of deep learning and PyTorch then read this book!" - Tiklu Ganguly Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorchSelect the right deep learning componentsTrain and evaluate a deep learning modelFine tune deep learning models to maximize performanceUnderstand deep learning terminologyAdapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped-you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. about the technologyDeep learning isn't just for big tech companies and academics. Anyone who needs to find meaningful insights and patterns in their data can benefit from these practical techniques! The unique ability for your systems to learn by example makes deep learning widely applicable across industries and use-cases, from filtering out spam to driving cars. about the bookInside Deep Learning is a fast-paced beginners' guide to solving common technical problems with deep learning. Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theory. You'll learn how deep learning works through plain language, annotated code and equations as you work through dozens of instantly useful PyTorch examples. As you go, you'll build a French-English translator that works on the same principles as professional machine translation and discover cutting-edge techniques just emerging from the latest research. Best of all, every deep learning solution in this book can run in less than fifteen minutes using free GPU hardware! about the readerFor Python programmers with basic machine learning skills. about the authorEdward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. His research includes deep learning, malware detection, reproducibility in ML, fairness/bias, and high performance computing. He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department. Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences. Bestandsnummer des Verkäufers LU-9781617298639
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: As New. Unread book in perfect condition. Bestandsnummer des Verkäufers 43037542
Anzahl: Mehr als 20 verfügbar
Anbieter: Basi6 International, Irving, TX, USA
Zustand: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Bestandsnummer des Verkäufers ABEOCT25-12538