Martinez-Ramon M Deep Learning A Practical Introduction 2024
Category: eBooks
- views: 108
- date: 27 July 2024
- posted by: BeMyLove
Martinez-Ramon M Deep Learning A Practical Introduction 2024 | 15.72 MB
405 Pages
Title: Deep Learning
Author: Manel Martinez-Ramon
Description:
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:
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.
DOWNLOAD:
https://rapidgator.net/file/6b0f1f048f0062a9a9c3e4cec4784c41/Martinez-Ramon_M._Deep_Learning._A_Practical_Introduction_2024.rar
https://k2s.cc/file/86ee7a003c53d/Martinez-Ramon_M._Deep_Learning._A_Practical_Introduction_2024.rar
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 tools
- Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures
- Practical discussions of recurrent neural networks and non-supervised approaches to deep learning
- Fulsome 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.
DOWNLOAD:
https://rapidgator.net/file/6b0f1f048f0062a9a9c3e4cec4784c41/Martinez-Ramon_M._Deep_Learning._A_Practical_Introduction_2024.rar
https://k2s.cc/file/86ee7a003c53d/Martinez-Ramon_M._Deep_Learning._A_Practical_Introduction_2024.rar
We need your support!
Make a donation to help us stay online
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem
Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u
USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm
Information |
|||
 |
Users of GUESTS are not allowed to comment this publication. | ||