MACHINE LEARNING with NEURAL NETWORKS using MATLAB

MACHINE LEARNING with NEURAL NETWORKS using MATLAB

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Artificial Neural Networks: Applications in Financial Forecasting

       
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igs816   发表于 2017-3-18 23:22:07 |只看作者 |倒序
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MACHINE LEARNING with NEURAL NETWORKS using MATLAB
2017 | English | ASIN: B06XC21FZV | 528 pages | EPUB | 6.7 Mb
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

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