• Title Deep Learning: Technical Introduction
• Author(s) Thomas Epelbaum
• Publisher: Arxiv (2017)
• Hardcover/Paperback N/A
• eBook PDF
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
• Share This:  

Book Description

This book 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. The forward pass and the update rules for the backpropagation algorithm are then derived in full.

It could be the first stop for deep learning beginners, as it contains lots of concrete, easy-to-follow examples with corresponding tutorial videos and code notebooks.

• The science behind deep learning
• Building and training your own neural networks
• Privacy concepts, including federated learning
• Tips for continuing your pursuit of deep learning

• N/A

• Amazon
• Amazon (Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence)

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

• Deep Learning: Technical Introduction (Thomas Epelbaum)
• The Mirror Site (1) - GitHub

• Deep Learning (Ian Goodfellow, et al)
• Deep Learning and the Game of Go (Max Pumperla, et al)
• Learning Deep Architectures for AI (Yoshua Bengio)
• Neural Networks and Deep Learning (Michael Nielsen)
• Deep Learning Tutorials (LISA Lab)