Modern Deep Learning Design and Application Development
Apress (Verlag)
978-1-4842-7412-5 (ISBN)
You’ll begin with a structured guide to using Keras, with helpful tips and best practices for making the most of the framework. Next, you’ll learn how to train models effectively with transfer learning and self-supervised pre-training. You will then learn how to use a variety of model compressions for practical usage. Lastly, you will learn how to design successful neural network architectures and creatively reframe difficult problems into solvable ones. You’ll learn notonly to understand and apply methods successfully but to think critically about it.
Modern Deep Learning Design and Methods is ideal for readers looking to utilize modern, flexible, and creative deep-learning design and methods. Get ready to design and implement innovative deep-learning solutions to today’s difficult problems.
What You’ll Learn
Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization.
Compress deep learning models while maintaining performance.
Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them.
Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches.
Who This Book Is For
Data scientists with some familiarity with deep learning to deep learning engineers seeking structured inspiration and direction on their next project. Developers interested in harnessing modern deep learning methods to solve a variety of difficult problems.
Andre Ye is a data science writer and editor; he has written over 300 data science articles for various top data science publications with over ten million views. He is also a cofounder at Critiq, a peer revision platform that uses machine learning to match users’ essays. In his spare time, Andre enjoys keeping up with current deep learning research, playing the piano, and swimming.
Chapter 1: A Deep Dive Into Keras.- Chapter 2: Pre-training Strategies and Transfer Learning.- Chapter 3: The Versatility of Autoencoders.- Chapter 4: Model Compression for Practical Deployment.- Chapter 5: Automating Model Design with Meta-Optimization.- Chapter 6:Successful Neural Network Architecture Design.- Chapter 7:Reframing Difficult Deep Learning Problems.
| Erscheinungsdatum | 02.12.2021 |
|---|---|
| Zusatzinfo | 204 Illustrations, black and white; XIX, 451 p. 204 illus. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Deep learning • Keras • Model compression • neural network • pre-trained model • pruning • Python • transfer learning |
| ISBN-10 | 1-4842-7412-1 / 1484274121 |
| ISBN-13 | 978-1-4842-7412-5 / 9781484274125 |
| Zustand | Neuware |
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