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Make Your Own Neural Network

Make Your Own Neural Network Author by : Tariq Rashid
Languange Used : en
Release Date : 2016-03-31
Publisher by : Createspace Independent Publishing Platform
ISBN : 1530826608
File Size : 48,8 Mb
Total Download : 664

A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also i
Category:

Neural Network Projects With Python

Neural Network Projects With Python Author by : James Loy
Languange Used : en
Release Date : 2019-02-28
Publisher by : Packt Publishing Ltd
ISBN : 9781789133318
File Size : 40,5 Mb
Total Download : 979

Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key Features Discover neural network architectures (like CNN and LSTM) that are driving recent advancements in AI Build expert neural networks in Python using popular libraries such as Keras Includes projects such as object detection, face identification, sentiment analysis, and more Book Description Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosi
Category: Computers

An Introduction To Neural Networks

An Introduction To Neural Networks Author by : Kevin Gurney
Languange Used : en
Release Date : 2018-10-08
Publisher by : CRC Press
ISBN : 9781482286991
File Size : 52,9 Mb
Total Download : 741

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world e
Category: Computers
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