Neural Networks and Deep Learning
by Michael Nielsen
2014
Number of pages: 235
Description:
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.
Download or read it online for free here:
Read online
(online html)
Similar books
Deep Learning Tutorial
by LISA lab - University of Montreal
This book will introduce you to some of the most important deep learning algorithms and show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.
(7869 views)
by LISA lab - University of Montreal
This book will introduce you to some of the most important deep learning algorithms and show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.
(7869 views)
Deep Learning
by Yoshua Bengio, Ian Goodfellow, Aaron Courville - MIT Press
This book can be useful for the university students learning about machine learning and the practitioners of machine learning, artificial intelligence, data-mining and data science aiming to better understand and take advantage of deep learning.
(16472 views)
by Yoshua Bengio, Ian Goodfellow, Aaron Courville - MIT Press
This book can be useful for the university students learning about machine learning and the practitioners of machine learning, artificial intelligence, data-mining and data science aiming to better understand and take advantage of deep learning.
(16472 views)
Deep Learning: Technical Introduction
by Thomas Epelbaum - arXiv.org
This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent. For each network, their fundamental building blocks are detailed.
(5210 views)
by Thomas Epelbaum - arXiv.org
This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent. For each network, their fundamental building blocks are detailed.
(5210 views)
The Matrix Calculus You Need For Deep Learning
by Terence Parr, Jeremy Howard - arXiv.org
This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math.
(5022 views)
by Terence Parr, Jeremy Howard - arXiv.org
This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math.
(5022 views)