Machine Learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.
MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, dynamic system modeling and control and most machine learning techniques. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. 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.
The more important features are the following:
•Deep learning, including convolutional neural networks and autoencoders
•Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox)
•Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN)
•Unsupervised learning algorithms, including self-organizing maps and competitive layers
•Apps for data-fitting, pattern recognition, and clustering
•Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance
•Simulink® blocks for building and evaluating neural networks and for control systems applications
MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, dynamic system modeling and control and most machine learning techniques. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. 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.
The more important features are the following:
•Deep learning, including convolutional neural networks and autoencoders
•Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox)
•Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN)
•Unsupervised learning algorithms, including self-organizing maps and competitive layers
•Apps for data-fitting, pattern recognition, and clustering
•Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance
•Simulink® blocks for building and evaluating neural networks and for control systems applications
