Artificial Neural Networks
by B. Mehlig
Publisher: arXiv.org 2019
Number of pages: 206
Description:
These are lecture notes for my course on Artificial Neural Networks that I have given at Chalmers and Gothenburg University. This course describes the use of neural networks in machine learning: deep learning, recurrent networks, and other supervised and unsupervised machine-learning algorithms.
Download or read it online for free here:
Download link
(6MB, PDF)
Similar books
Machine Learning, Neural and Statistical Classificationby D. Michie, D. J. Spiegelhalter - Ellis Horwood
The book provides a review of different approaches to classification, compares their performance on challenging data-sets, and draws conclusions on their applicability to realistic industrial problems. A wide variety of approaches has been taken.
(28026 views)
Programming Neural Networks with Encog3 in Javaby Jeff Heaton - Heaton Research
The book is an introduction to Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques are also introduced.
(18393 views)
Neural Networksby Rolf Pfeifer, Dana Damian, Rudolf Fuchslin - University of Zurich
Systematic introduction to neural networks, biological foundations; important network classes and learning algorithms; supervised models (perceptrons, adalines, multi-layer perceptrons), support-vector machines, echo-state networks, etc.
(12015 views)
Recurrent Neural Networksby Xiaolin Hu, P. Balasubramaniam - InTech
The concept of neural network originated from neuroscience, and one of its aims is to help us understand the principle of the central nerve system through mathematical modeling. The first part of the book is dedicated to this aim.
(13792 views)