• Title: Deep Learning Tutorials
• Author(s) LISA Lab
• Publisher: University of Montreal
• Hardcover/Paperback: 409 pages
• eBook: HTML and PDF
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
• Share This:  

Book Description

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.

The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also 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.

• N/A

• Amazon

• Neural Networks and Deep Learning
• Machine Learning
• Data Science
• Artificial Intelligence
• Data Analysis and Data Mining

• Deep Learning Tutorials (LISA Lab)
• The Mirror Site (1) - PDF

• Understanding Deep Learning (Simon J.D. Prince)

  An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Only the most important ideas to provide a high density of critical information in an intuitive and digestible form.

• Manifold Learning: Model Reduction in Engineering

  The aim is to provide tools for a better understanding and implement reduced order models by using: physics-based models, synthetic data forecast by these models, experimental data and deep learning algorithms.

• The Principles of Deep Learning Theory: An Effective Approach

  This textbook establishes a theoretical framework for understanding deep learning models of practical relevance, provide clear and pedagogical explanations of how realistic deep neural networks actually work.

• The Little Book of Deep Learning (François Fleuret)

  This book is a short introduction to deep learning for readers with a STEM background, originally designed to be read on a phone screen, is limited to the background necessary to understand a few important models.

• The Shallow and the Deep: Introduction to Neural Networks

  This book is a collection of lecture notes that offers an accessible introduction to Neural Networks and machine learning in general. The focus lies on classical machine learning techniques, with a bias towards classification and regression.

• Gradient Expectations: Structure of Predictive Neural Networks

  An insightful investigation into the mechanisms underlying the predictive functions of neural networks - and their ability to chart a new path for AI. Delve into the known neural architecture of the mammalian brain to illuminate the structure of predictive networks.

• Mathematical Introduction to Deep Learning (Arnulf Jentzen, et al)

  This book aims to provide an introduction to the topic of deep learning algorithms, coverss essential components of deep learning algorithms in full mathematical detail including different Artificial Neural Network (ANN) architectures and algorithms.

• Deep Learning (Ian Goodfellow, et al)

  Written by three experts, this is the only comprehensive book on the subject. It offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

• Neural Networks and Deep Learning (Michael Nielsen)

  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.