Data Mining, Big Data Analytics And Machine Learning With Neural Networks. Examples With Matlab
Perez, C.
Data Mining, Big Data Analytics And Machine Learning With Neural Networks. Examples With Matlab - 1A. ed - ESTADOS UNIDOS AMAZON 2019 - 387 23.0
Data Mining, Big Data Analytics And Machine Learning
. -- Introduction To Data Mining Techniques
. -- Introduction To Neural Networks
. -- Introduction To Big Data Analytics
. -- Big Data Analytics And Matlab
. -- Introduction To Machine Lerarning
. -- Machine Learning Techniques
. -- Machine Learning And Other Tecniques
. -- Neural Networks And Machine Learning With Matläb
. -- Matlab Neural Network Toolbox (Deep Learning Toolbox From Version 18) And
. -- Machine Learning
. -- Using Neural Network Toolbox (Deep Learning Toolbox From Version 18)
. -- Automatic Script Generation
. -- Neural Network Toolbox (Deep Learning Toolbox From Version 18) Applications
. -- Neural Network Design Steps
. -- Supervised Learning: Multilayer Neural Neovork
. -- Neural Network Architectures
. -- One Layer Of Neurons
. -- Multiple Layers Of Neurons
. -- Input And Output Processing Functions
. -- Multilayer Neural Networks And Backpropagation Training
. -- Multilayer Neural Network Architecture
. -- Neuron Model (Logsig. Tansig. Purelin)
. -- Feedforward Neural Network
. -- Understanding Neural Network Toolbox (Deep Learning Toolbox From Version 18) Data Structures
. -- Simulation With Concurrent Inputs In a Static Network
. -- Simulation With Sequential Inputs In a Dynamic Network
. -- Simulation With Concurrent Inputs In a Dynamic Network
. -- Supervised Learning: Fitting Neural Networks. Fit Regression Models
. -- Function Fiitincj Neural Network. Examples
. -- Construct And Train a Function Fitting Network
. -- Create And Train Feedforward Neural Network
. -- Create And Train a Cascade Network
. -- Nefwork Performance
. -- Description
. -- Examples Fit Regression Model And Plot Fitfed Values Versus Targeis.Examples Description Examples Plot Output And Target Values. Examples Description Examples Plot Trainng State Values. Examples Plot Performance. Examples Plot Histogram Of Error Values. Examples Syntax Description Examples Generate Matlab Function For Simulation Neural Network. Examples Create Functions From Static Neural Network Create Functions From Dynamic Neural Network a Complete Example: House Price Estimation The Problem: Estimate I Louse Values Why Neural Networks? Preparing The Data Fitting a Function With a Neural Network Testing The Neural Network Supervised Learning: Fit Data With a Neural Network. Graphical Interface Intruduction Using The Neural Network Fitting Tool Using Command-Line Functions Supervised Learning: Perceptron Neural Networks Intruduction Neuron Model Perceptron Architecture Create a Perceptron Perceptron Learning Rule (Learnp) Training (Train) Limitation And Cautions Perceptron Examples Classification With a 2 Input Perceptron Outlier Input Vector Normalized Rule Linearly Non-Separable Vectors Supervised Learning: Radial Basis Neural Networks? Radial Basis Function Network Neuron Model Network Architecture Exact Design (Newrbe) More Efficient Design (Newrb) Radial Basis Examples Radial Basis Approximation Radial Basis Underlapping Neurons Grnn Function Approximation
. -- Pnn Classification Supervised Learning: Generalized Regresion And Lvq Neural Networks Generalized Regression Neural Networks Network Architecture Design (Newgrnn) Learning Vector Quantization (Lvq) Neural Neiworks Architecture Creating An Lvq Network Lvq1 Learning Rule Training Supplemental Supervised Learning: Hopfield And Linear Neural Networks Linear Neural Networks Neuron Model Network Architecture Create a Linear Neuron (Linearlayer) Least Mean Square Error Linear System Design (Newlind) Linear Networks With delays Lms Algorithm (Learnwh) Linear Classification (Train) Limitations And Cautions Hopfield Neural Network Fundamentals Architecture Design (Newhop) Summary Linear Prediction Design Example159 Defining a Wave Form Setting Up The Problem For a Neural Network Designing The Linear Layer Testing The Linear Layer Adaptive Linear Prediction Example162 Defining a Wave Form Setting Up The Problem For a Neural Network Creating The Linear Layer Adapting The Linear Layer Hopfield Two Neuron Design Example Hopfield Unstable Equilibria Example Hopfield Three Neuron Example Hopfield Spurious Example Supervised Learning Time Series Neural Networks Modeling And Prediction With Narx And Time delay Networks Functions For Model And Prediction Timedelaynet Narxnet Narxnet Layrecnet Distdelayney
. -- Train Usingcommand Line Funcion Example. Maglev Modeling Ne Problem: Model a Magnetic Levitation System Neural Networks Preparing The Data Time Series Modelling With a Neural Network Testing The Neural Network Supervised Learning: Neural Network Time-Series Prediction And Modeling. Graphical Interface Introduction Using Neural Network Time Series Tool Using Command-Line Functions Sunsupervised Learning: Network One-Dimensional Self-Organizing Map Two-Dimensional Self-Organizing Map Training With The Batch Algorithm Selforgmap Plotsomhits Plotsomnc Plotsomnd Gexpression Analysis. Analysis And Principal Components The Promblem: Analyzing Gene Expressions In Baker's Yeast (Saccharomyces Cerevisiae) Data Filtering The Genes Principal Component Analysis Cluster Analysis Using Principal Components: Self-Organizing Maps Compeitiive Learning One-Dimensional Map Invo-Dimensional Self-Organizing Map Create a Competitive Neural Network. Bias And Kohonen Learning Rule Kohonen Leaming Rule (Learnk) Bias Leaming Rule (Learncon) Training Graphical Example Competitive Layers Functions Competlayer View Trainru Learnk Leamcon Unsupervised Learning: Cluser Data a Self-Organizing Map Graphical Interface Introduction Using The Neural Network Clustering Tool Using Command-Line Functions Unsupervised Learning: Pattern Recognitn And Classihcation Neural Networks Deep Learning Introduction Functions For Paitewrn Recognition And Classification. Examples View Neuralnetwork
. -- Patten Recognition Pattem Recognition Leaming Vector Quantimtion Training Options And Network Receiver Operating Characteristic: Roc Plot Receiver Operating Characteristic: Plotroc Plot Classification Confusion Matrix: Plotconfusim Neural Network Performance: Crossentropy Deep Learning I Trainautoencoder Construct Deep Network Using Autoencoders Decode Encode Predict Stack Train Stacked Autocoders For Image Clasificacion. Deep Neural Network Dataset Training The First Autoencoder Visualizing The Weights Of The First Autoencoder Training The Second Autoencoder Training The Final Softmax Layer Forming a Stacked Neural Network Fine Tuning The Deep Neural Network Summary Transfer Learning Using Convolucional Neural Crab Classification Why Neural Networks Preparing The Data Building The Neural Network Classifier Testing The Classifier Wine Classificaiion. Pattern Recognition Problem: Classify Wines Why Neural Networks9 Preparing The Data Pattern Recognition With a Neural Network Testing The Neural Network Cancer Detection Formatting The Data Ranking Key Features Classification Using a Feed Forward Neural Network Character Recognition Creating The First Neural Network Training The First Neural Network Training The Second Neural Testing Both Neural Networks Learning Vector Quantization (Lvq). Example Unsupervised Learning: Classify Patterns With a Neural Network. Graphical Interface Introduction Using The Neural Network Pattern Recognition Tool Using Command-Line Functions
DATA MINING, BIG DATA ANALYTICS AND MACHINE LEARNING
005.74.PERE.05
Data Mining, Big Data Analytics And Machine Learning With Neural Networks. Examples With Matlab - 1A. ed - ESTADOS UNIDOS AMAZON 2019 - 387 23.0
Data Mining, Big Data Analytics And Machine Learning
. -- Introduction To Data Mining Techniques
. -- Introduction To Neural Networks
. -- Introduction To Big Data Analytics
. -- Big Data Analytics And Matlab
. -- Introduction To Machine Lerarning
. -- Machine Learning Techniques
. -- Machine Learning And Other Tecniques
. -- Neural Networks And Machine Learning With Matläb
. -- Matlab Neural Network Toolbox (Deep Learning Toolbox From Version 18) And
. -- Machine Learning
. -- Using Neural Network Toolbox (Deep Learning Toolbox From Version 18)
. -- Automatic Script Generation
. -- Neural Network Toolbox (Deep Learning Toolbox From Version 18) Applications
. -- Neural Network Design Steps
. -- Supervised Learning: Multilayer Neural Neovork
. -- Neural Network Architectures
. -- One Layer Of Neurons
. -- Multiple Layers Of Neurons
. -- Input And Output Processing Functions
. -- Multilayer Neural Networks And Backpropagation Training
. -- Multilayer Neural Network Architecture
. -- Neuron Model (Logsig. Tansig. Purelin)
. -- Feedforward Neural Network
. -- Understanding Neural Network Toolbox (Deep Learning Toolbox From Version 18) Data Structures
. -- Simulation With Concurrent Inputs In a Static Network
. -- Simulation With Sequential Inputs In a Dynamic Network
. -- Simulation With Concurrent Inputs In a Dynamic Network
. -- Supervised Learning: Fitting Neural Networks. Fit Regression Models
. -- Function Fiitincj Neural Network. Examples
. -- Construct And Train a Function Fitting Network
. -- Create And Train Feedforward Neural Network
. -- Create And Train a Cascade Network
. -- Nefwork Performance
. -- Description
. -- Examples Fit Regression Model And Plot Fitfed Values Versus Targeis.Examples Description Examples Plot Output And Target Values. Examples Description Examples Plot Trainng State Values. Examples Plot Performance. Examples Plot Histogram Of Error Values. Examples Syntax Description Examples Generate Matlab Function For Simulation Neural Network. Examples Create Functions From Static Neural Network Create Functions From Dynamic Neural Network a Complete Example: House Price Estimation The Problem: Estimate I Louse Values Why Neural Networks? Preparing The Data Fitting a Function With a Neural Network Testing The Neural Network Supervised Learning: Fit Data With a Neural Network. Graphical Interface Intruduction Using The Neural Network Fitting Tool Using Command-Line Functions Supervised Learning: Perceptron Neural Networks Intruduction Neuron Model Perceptron Architecture Create a Perceptron Perceptron Learning Rule (Learnp) Training (Train) Limitation And Cautions Perceptron Examples Classification With a 2 Input Perceptron Outlier Input Vector Normalized Rule Linearly Non-Separable Vectors Supervised Learning: Radial Basis Neural Networks? Radial Basis Function Network Neuron Model Network Architecture Exact Design (Newrbe) More Efficient Design (Newrb) Radial Basis Examples Radial Basis Approximation Radial Basis Underlapping Neurons Grnn Function Approximation
. -- Pnn Classification Supervised Learning: Generalized Regresion And Lvq Neural Networks Generalized Regression Neural Networks Network Architecture Design (Newgrnn) Learning Vector Quantization (Lvq) Neural Neiworks Architecture Creating An Lvq Network Lvq1 Learning Rule Training Supplemental Supervised Learning: Hopfield And Linear Neural Networks Linear Neural Networks Neuron Model Network Architecture Create a Linear Neuron (Linearlayer) Least Mean Square Error Linear System Design (Newlind) Linear Networks With delays Lms Algorithm (Learnwh) Linear Classification (Train) Limitations And Cautions Hopfield Neural Network Fundamentals Architecture Design (Newhop) Summary Linear Prediction Design Example159 Defining a Wave Form Setting Up The Problem For a Neural Network Designing The Linear Layer Testing The Linear Layer Adaptive Linear Prediction Example162 Defining a Wave Form Setting Up The Problem For a Neural Network Creating The Linear Layer Adapting The Linear Layer Hopfield Two Neuron Design Example Hopfield Unstable Equilibria Example Hopfield Three Neuron Example Hopfield Spurious Example Supervised Learning Time Series Neural Networks Modeling And Prediction With Narx And Time delay Networks Functions For Model And Prediction Timedelaynet Narxnet Narxnet Layrecnet Distdelayney
. -- Train Usingcommand Line Funcion Example. Maglev Modeling Ne Problem: Model a Magnetic Levitation System Neural Networks Preparing The Data Time Series Modelling With a Neural Network Testing The Neural Network Supervised Learning: Neural Network Time-Series Prediction And Modeling. Graphical Interface Introduction Using Neural Network Time Series Tool Using Command-Line Functions Sunsupervised Learning: Network One-Dimensional Self-Organizing Map Two-Dimensional Self-Organizing Map Training With The Batch Algorithm Selforgmap Plotsomhits Plotsomnc Plotsomnd Gexpression Analysis. Analysis And Principal Components The Promblem: Analyzing Gene Expressions In Baker's Yeast (Saccharomyces Cerevisiae) Data Filtering The Genes Principal Component Analysis Cluster Analysis Using Principal Components: Self-Organizing Maps Compeitiive Learning One-Dimensional Map Invo-Dimensional Self-Organizing Map Create a Competitive Neural Network. Bias And Kohonen Learning Rule Kohonen Leaming Rule (Learnk) Bias Leaming Rule (Learncon) Training Graphical Example Competitive Layers Functions Competlayer View Trainru Learnk Leamcon Unsupervised Learning: Cluser Data a Self-Organizing Map Graphical Interface Introduction Using The Neural Network Clustering Tool Using Command-Line Functions Unsupervised Learning: Pattern Recognitn And Classihcation Neural Networks Deep Learning Introduction Functions For Paitewrn Recognition And Classification. Examples View Neuralnetwork
. -- Patten Recognition Pattem Recognition Leaming Vector Quantimtion Training Options And Network Receiver Operating Characteristic: Roc Plot Receiver Operating Characteristic: Plotroc Plot Classification Confusion Matrix: Plotconfusim Neural Network Performance: Crossentropy Deep Learning I Trainautoencoder Construct Deep Network Using Autoencoders Decode Encode Predict Stack Train Stacked Autocoders For Image Clasificacion. Deep Neural Network Dataset Training The First Autoencoder Visualizing The Weights Of The First Autoencoder Training The Second Autoencoder Training The Final Softmax Layer Forming a Stacked Neural Network Fine Tuning The Deep Neural Network Summary Transfer Learning Using Convolucional Neural Crab Classification Why Neural Networks Preparing The Data Building The Neural Network Classifier Testing The Classifier Wine Classificaiion. Pattern Recognition Problem: Classify Wines Why Neural Networks9 Preparing The Data Pattern Recognition With a Neural Network Testing The Neural Network Cancer Detection Formatting The Data Ranking Key Features Classification Using a Feed Forward Neural Network Character Recognition Creating The First Neural Network Training The First Neural Network Training The Second Neural Testing Both Neural Networks Learning Vector Quantization (Lvq). Example Unsupervised Learning: Classify Patterns With a Neural Network. Graphical Interface Introduction Using The Neural Network Pattern Recognition Tool Using Command-Line Functions
DATA MINING, BIG DATA ANALYTICS AND MACHINE LEARNING
005.74.PERE.05