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TORRENT DETAILS
Udemy - Neural Networks Made Easy With Matlab
TORRENT SUMMARY
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MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language developed by MathWorks. Although MATLAB is intended primarily for numerical computing, but by optional toolboxes, using the MuPAD symbolic engine, has access to symbolic computing capabilities too. One of these toolboxes is Neural Network toolbox. This toolbox is free, open source software for simulating models of brain and central nervous system, based on MATLAB computational platform. In these courses you will learn the general principles of Neural Network Toolbox designed in Matlab and you will be able to use this Toolbox efficiently as well. Medium MATLAB (Matrix Laboratory) several paradigms numerical computing environment and the fourth generation programming language designed mathworks. While in MATLAB is intended primarily for numerical computation, but an optional set of tools with MuPAD symbolic engine has access to the symbolic computation capabilities, too. One such tool is the neural network tool. This tool is a free, open source software for the simulation model of the brain and central nervous system, based on MATLAB computing platforms. In these courses you will learn the general principles of neural network toolbox developed in MATLAB and you can use this tool effectively, as well.
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FILE LIST
Filename
Size
1- Chapter 1/1- Introduction.mp4
4.4 MB
1- Chapter 1/2- What is in this course .mp4
6.2 MB
1- Chapter 1/3- Function Fitting.mp4
12 MB
1- Chapter 1/4- Pattern Recognition.mp4
18 MB
1- Chapter 1/5- Data Clustering.mp4
11 MB
2- Chapter 2/6- Simple Neuron.mp4
11.9 MB
2- Chapter 2/7- Network architecture.mp4
7.1 MB
2- Chapter 2/8- Data structure.mp4
11.7 MB
2- Chapter 2/9- Training style.mp4
23.7 MB
3- Chapter 3/10- Neuron Model.mp4
23.8 MB
3- Chapter 3/11- Perceptron networks.mp4
12.9 MB
3- Chapter 3/12- GUI nntool.mp4
11.7 MB
4- Chapter 4/13- Network architecture.mp4
8.4 MB
4- Chapter 4/14- Linear filters and linear classification.mp4
15.3 MB
5- Chapter 5/15- Introduction to Training Process.mp4
12 MB
5- Chapter 5/16- Back Propagation Architecture.mp4
