Könyv UNDERSTANDING DEEP LEARNING PRINCE SIMON J D

UNDERSTANDING DEEP LEARNING

Szerző: PRINCE SIMON J D
Nyelv: Angol
Kötés: Kemény kötésű
Kiadó: MIT
Elérhetőség: Beszállítói készleten
Küldés 3-6 napon belül
37 770 Ft
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic mid...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2023
oldal
544
EAN
9780262048644
Enbook ID
43321618
Kiadó
MIT
Súly
1316

Teljes leírás

An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.

Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.

  • Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models
  • Short, focused chapters progress in complexity, easing students into difficult concepts 
  • Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models
  • Streamlined presentation separates critical ideas from background context and extraneous detail
  • Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible 
  • Programming exercises offered in accompanying Python Notebooks 

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