**Author**: César Pérez López

**Publisher:** Lulu Press, Inc

**ISBN:** 1716812321

**Category : **Computers

**Languages : **en

**Pages : **
**Get Book**

**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:** 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**: C Lorentz

**Publisher:**
**ISBN:**
**Category : **
**Languages : **en

**Pages : **280

**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 in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering and pattern recognition are the most common unsupervised learning techniques. It is posible use this techniques for exploratory data analysis, to find hidden patterns or groupings in data and to make all kind of segmentations.

**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**: 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 Lorentz

**Publisher:**
**ISBN:**
**Category : **
**Languages : **en

**Pages : **172

**View**

**Book Description**
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, dimensionality reduction, time-series forecasting, dynamic system modeling and control and most machine learning techniques. 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. This book deeps in unsupervised learning techniques 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:** 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**: C Lorentz

**Publisher:**
**ISBN:**
**Category : **
**Languages : **en

**Pages : **218

**View**

**Book Description**
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, dimensionality reduction, time-series forecasting, dynamic system modeling and control and most machine learning techniques. 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. This book deeps in supervised learning techniques across Neural Networks.

**Author**: C. Perez

**Publisher:** Independently Published

**ISBN:** 9781092551939

**Category : **Computers

**Languages : **en

**Pages : **382

**View**

**Book Description**
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, 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

**Author**: César Pérez López

**Publisher:** Lulu.com

**ISBN:** 9781716808289

**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. 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.