You might also like

More about Deep Learning: A Practical Introduction

• Book details
• Synopsis
• Trending among others

You get 247 loyalty points

Book synopsis

An engaging and accessible introduction to deep learning perfect for students and professionals In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning toolsComprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architecturesPractical discussions of recurrent neural networks and non-supervised approaches to deep learningFulsome treatments of generative adversarial networks as well as deep Bayesian neural networks Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.

Book details

• Full title: Deep Learning: A Practical Introduction
• Author: Manel Martinez-Ramon, Aswathy Rajendra Kurup, Ajith Meenu
• Language: English
• Binding: Hardback
• Number of pages: 300
• EAN: 9781119861867
• ISBN: 1119861861
• ID: 37029607
• Publisher: John Wiley and Sons Ltd
• Weight: 872 g
• Dimensions: 244 × 170 mm
• Date of publishing: 26. October 2023

Trending among others

---

---

---