Free An Introduction To Neural Networks Ebooks Online

                
              
Free 
An Introduction To Neural Networks Ebooks Online
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File Size: 8201 KB
Print Length: 148 pages
Page Numbers Source ISBN: 1857285034
Simultaneous Device Usage: Up to 4 simultaneous devices, per publisher limits
Publisher: UCL Press (March 24, 2009)
Publication Date: March 24, 2009
Sold by:  Digital Services LLC
Language: English
ASIN: B0023ZLHMA
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Best Sellers Rank: #887,386 Paid in Kindle Store (See Top 100 Paid in Kindle Store)   #159 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Neural Networks   #205 in Kindle Store > Kindle eBooks > Computers & Technology > Systems Analysis & Design   #681 in Books > Computers & Technology > Computer Science > Systems Analysis & Design
I checked this book out of my university library and was pleasently suprised, although this is by no means a comprehensive book for those who are taking a high level course on AI/neural nets it serves as a very good and complete introduction to those of us who wish to learn more about a very interesting area of computing. After an introduction and review of notation, several basic models are introduced starting with the TLU and progressively presenting more advanced models.The book is not aimed towards computer science students, and also has in mind other backgrounds. It does however require a sufficient background in science/math (basic algebra and geometry, vectors).
This is one of the best written books on NN. This book has that rare quality of being succinct but clearly written so that it can be understood by reasonably mathematical minded individual.It covers most of the basic topics (back propagation, feed forward, Hopfield nets etc) and gives idiosyncrasies of the field. It also gives you a simple but accurate understanding of the mathematics and algorithms for the field.I would highly recommend this book to anyone just entering into the field and has some mathematical background.
This book got me started with neural networks. Prior to this book I had only read some articles and didn't quite know what was going on. Now I have an application that makes football predictions straight up. It does not go overboard with math but there are certainly some deep sections. Even if you are using someone else's neural network objects, this is a good read to help you understand the concepts behind NN and what type you want to use.
An excellent introduction to the subject. The author does a good job of presenting the core ideas in as intuitive a manner as possible without dumbing down the subject. Rigorous math is avoided making this an excellent introductory text for those wishing to grasp the fundamental concepts, and understand the power and practicality of neural networks. I would recommend this book as a companion to Simon Haykin's Neural Networks: A Comprehensive Foundation.
Deep Learning: Natural Language Processing in Python with Recursive Neural Networks: Recursive Neural (Tensor) Networks in Theano (Deep Learning and Natural Language Processing Book 3) Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks (MIT Press) Deep Learning for Business with R: A Very Gentle Introduction to Business Analytics Using Deep Neural Networks Deep Learning Step by Step with Python: A Very Gentle Introduction to Deep Neural Networks for Practical Data Science Introduction to the Math of Neural Networks An Introduction to Neural Networks Principles of Neural Science, Fifth Edition (Principles of Neural Science (Kandel)) Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python) Unsupervised Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python and Theano (Machine Learning in Python) Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow (Machine Learning in Python) Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow (Machine Learning in Python) Deep Learning Neural Networks: Design and Case Studies Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications (International Series on Computational Intelligence) Kalman Filtering and Neural Networks Neural Networks: A Comprehensive Foundation (2nd Edition) Elements of Artificial Neural Networks (Complex Adaptive Systems) Intelligence Emerging: Adaptivity and Search in Evolving Neural Systems (MIT Press) Soft Computing: Integrating Evolutionary, Neural, and Fuzzy Systems Neural Network Training Using Genetic Algorithms (Series in Machine Perception and Artificial Intelligence) 