Modern Deep Learning Design and Application Development: Versatile Tools to Solve Deep Learning Problems - Tapa blanda

Acerca del autor

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.

De la contraportada

Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking.  

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. 

You will:

• 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.