Jonathan Tran; Prateek Puri; Jordan Logue; Anthony Jacques; Li Ang Zhang; Krista Langeland; George Nacouzi; Gary Briggs RAND Corporation (2024) Pehmeäkantinen kirja
Jian Li (ed.); Mingming Zhang (ed.); Bowen Li (ed.); Sergio Neves Monteiro (ed.); Shadia Ikhmayies (ed.); Yunus Eren Kalay Springer (2022) Pehmeäkantinen kirja
Bowen Li (ed.); Jian Li (ed.); Shadia Ikhmayies (ed.); Mingming Zhang (ed.); Yunus Eren Kalay (ed.); John S. (ed Carpenter Springer (2019) Kovakantinen kirja
Jian Li (ed.); Mingming Zhang (ed.); Bowen Li (ed.); Sergio Neves Monteiro (ed.); Shadia Ikhmayies (ed.); Yunus Eren Kalay Springer (2021) Pehmeäkantinen kirja
Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse fields as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires you to simultaneously understand how to cast a problem, the basic mathematics of modeling, the algorithms for fitting your models to data, and the engineering techniques to implement it all. This book is a comprehensive resource that makes deep learning approachable, while still providing sufficient technical depth to enable engineers, scientists, and students to use deep learning in their own work. No previous background in machine learning or deep learning is required—every concept is explained from scratch and the appendix provides a refresher on the mathematics needed. Runnable code is featured throughout, allowing you to develop your own intuition by putting key ideas into practice.
