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Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. Supervised learning uses classification and regression techniques to develop predictive models. MATLAB has the tool Deep Learning Toolbox (Neural Network Toolbox for versions before 18) that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, pattern recognition, dimensionality reduction, time-series forecasting, dynamic system modeling and control and most machine learning techniques. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox
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Supervised Learning Techniques: Function Approximation And Non Linear Regression With Neural Networks. Examples With Matlab
Bokrecensioner » Supervised Learning Techniques: Function Approximation And Non Linear Regression With Neural Networks. Examples With Matlab
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