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Neural Networks: Introduction to Artificial Neurons, Backpropagation and Multilayer Feedforward Neural Networks with Real-World Applications
Paperback
Series: Advanced Data Analytics, Book 2
Publisher: Independently Published
Published: May 6 2020
Pages: 54
Weight: 0.19
Height: 0.11 Width: 5.98 Depth: 9.02
Language: English
Software Engineers recognize that computers can process and store much more data than humans, yet even supercomputers can't carry out tasks that come easily to the human brain, such as facial recognition or natural language processing. MIT's state-of-the-art research facility, named Centre for Brains, Minds and Machines, is a perfect testimonial to this fundamental interaction between the human brain and computers in today's world.
Hence engineers began studying the processes and structures of our human brains, hoping to build a computer model of its functions - Neural Networks were born. These models are very simplistic, but fundamentally replicate the inner structures of our own brains downright to the functions of an individual neuron
In this book I show you exactly how engineres model the inner functions and structure of the human brains, covering the fundamental mathematical equations and underlying concepts. In particular, you will learn about...
- How to Build a Computer model of a Brain Cell (or Neuron)
- The Fundamental properties of a Neural Network
- Multilayer Forward Networks
- Using the Backpropagation algorithm to learn and adapt
- Counter Propagation Networks
- How to train a Neural network (validation and testing techniques to avoid overfitting)
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