**Author**: César Pérez López

**Publisher:** Lulu Press, Inc

**ISBN:** 1794891439

**Category : **Mathematics

**Languages : **en

**Pages : **
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**Book Description**
Deep Learning techniques examines large amounts of data to uncover hidden patterns, correlations and other insights using Neural Netwrks. MATLAB has the tool Neural Network Toolbox (Deep Learning Toolbox from version 18) 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, and dynamic system modeling and control. 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 Big Data tools (Parallel Computing Toolbox). 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. This book develops cluster analysis and pattern recognition across Neural Networks.

**Author**: César Pérez López

**Publisher:** Lulu Press, Inc

**ISBN:** 1794891439

**Category : **Mathematics

**Languages : **en

**Pages : **
**View**

**Book Description**
Deep Learning techniques examines large amounts of data to uncover hidden patterns, correlations and other insights using Neural Netwrks. MATLAB has the tool Neural Network Toolbox (Deep Learning Toolbox from version 18) 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, and dynamic system modeling and control. 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 Big Data tools (Parallel Computing Toolbox). 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. This book develops cluster analysis and pattern recognition across Neural Networks.

**Author**: C. PEREZ

**Publisher:** Lulu Press, Inc

**ISBN:** 171687582X

**Category : **Computers

**Languages : **en

**Pages : **
**View**

**Book Description**
Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. MATLAB has the tool Neural Network Toolbox (Deep Learning Toolbox from version 18) 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, and dynamic system modeling and control.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 Big Data tools (Parallel Computing Toolbox). 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. This book develops cluster analysis and pattern recognition

**Author**: César Pérez López

**Publisher:** Lulu Press, Inc

**ISBN:** 1716811872

**Category : **Computers

**Languages : **en

**Pages : **
**View**

**Book Description**
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. Unsupervised learning finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition. This book develops cluster analysis techniques.

**Author**: C Perez

**Publisher:** Independently Published

**ISBN:** 9781096862611

**Category : **
**Languages : **en

**Pages : **218

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**Book Description**
MATLAB has the tool Neural Network Toolbox (Deep Learning Toolbox from version 18) 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, and dynamic system modeling and control.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.This book develops statistics and data analysis methods for cluster analysis and pattern recognition with neural networks using MATLAB. the most important topics are the next: CLUSTER DATA WITH NEURAL NETWORKSCLUSTER WITH SELF-ORGANIZING MAP NEURAL NETWORKSELF-ORGANIZING MAPS. FUNCTIONSCOMPETITIVE NEURAL NETWORKSCOMPETITITVE LAYERSCLASSIFY PATTERNS WITH A NEURAL NETWORKFUNCTIONS FOR PATTERN RECOGNITION AND CLASSIFICATIONCLASSIFICATION WITH NEURAL NETWORKS. EXAMPLE

**Author**: César Pérez López

**Publisher:** Lulu Press, Inc

**ISBN:** 1678018678

**Category : **Technology & Engineering

**Languages : **en

**Pages : **
**View**

**Book Description**
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, and dynamic system modeling and control. 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.

**Author**: K. Taylor

**Publisher:** Createspace Independent Publishing Platform

**ISBN:** 9781545247303

**Category : **
**Languages : **en

**Pages : **416

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**Book Description**
Cluster analisys is a set of unsupervised learning techniques to find natural groupings and patterns in data. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics. Cluster analysis, also called segmentation analysis or taxonomy analysis, partitions sample data into groups or clusters. Clusters are formed such that objects in the same cluster are very similar, and objects in different clusters are very distinct. MATLAB Statistics and Machine Learning Toolbox provides several clustering techniques and measures of similarity (also called distance measures) to create the clusters. Additionally, cluster evaluation determines the optimal number of clusters for the data using different evaluation criteria. Cluster visualization options include dendrograms and silhouette plots. Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. The more important topics in this book are de following: Cluster analisys. Hierarchical clustering Cluster analisys. Non hierarchical clustering Cluster analisys. Gaussian mixture models and hidden markov models Cluster analisys. Nearest neighbors. KNN classifiers Cluster visualization and evaluation Cluster data with neural networks Cluster with self-organizing map neural network Self-organizing maps. Functions Competitive neural networks Competitive layers Classify patterns with a neural network Functions for pattern recognition and classification Classification with neural networks. Examples Autoencoders and clustering with neural networks. Examples Self-organizing networks. Examples

**Author**: César Pérez López

**Publisher:** Lulu Press, Inc

**ISBN:** 1716812321

**Category : **Computers

**Languages : **en

**Pages : **
**View**

**Book Description**
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. Unsupervised learning finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition. This book develops pattern recognition techniques.

**Author**: César Pérez López

**Publisher:** Lulu Press, Inc

**ISBN:** 1716808626

**Category : **Computers

**Languages : **en

**Pages : **
**View**

**Book Description**
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. The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models. • Classification techniques predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, image and speech recognition, and credit scoring. • Regression techniques predict continuous responses, for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading. This book develops time series forecasting techniques using neural networks

**Author**: Giuseppe Ciaburro

**Publisher:**
**ISBN:** 9781788398435

**Category : **
**Languages : **en

**Pages : **382

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**Book Description**
Extract patterns and knowledge from your data in easy way using MATLABAbout This Book* Get your first steps into machine learning with the help of this easy-to-follow guide* Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB* Understand how your data works and identify hidden layers in the data with the power of machine learning.Who This Book Is ForThis book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well.What You Will Learn* Learn the introductory concepts of machine learning.* Discover different ways to transform data using SAS XPORT, import and export tools,* Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data.* Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment.* Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures.* Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox.* Learn feature selection and extraction for dimensionality reduction leading to improved performance.In DetailMATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners.You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions.You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement.At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB.Style and approachThe book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.

**Author**: CESAR PEREZ LOPEZ

**Publisher:** Lulu Press, Inc

**ISBN:** 171680521X

**Category : **Computers

**Languages : **en

**Pages : **
**View**

**Book Description**
Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. Some of the most successful deep learning methods involve artificial neural networks. In a deep network, there are many layers between the input and output (and the layers are not made of neurons but it can help to think of it that way), allowing the algorithm to use multiple processing layers, composed of multiple linear and non-linear transformations. MATLAB has the tool Deep Learning Toolbox (Neural Networks Toolbox in versions prior to release 2018) 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, and dynamic system modeling and control. This book develops the mentioned tasks.