MATLAB Books
Deep Learning
- Deep Learning
  
  Deep Learning for Image Recognition
  
  Object Classification in Photographs
  
  Pretrained Networks
  
  Classifications using a network already created and trained
  
  Identify Objects in Some Images
  
  Making Predictions
  
  CNN Architecture
  
  Investigating Predictions
  
  Image Datastores
  Preprocessing Images
  
  Preparing Images to Use as Input
  
  Augmenting Images in a Datastore
  
  Performing Transfer Learning
  
  Preparing Training Data
  
  Modifying Network Layers
  
  Training Options
  
  Evaluating Performance
  
  Transfer Learning
Machine Learning
- Classification Models
  
  Supervised learning techniques to perform predictive modeling for classification problems.
  
  Predictive Models
  
  Reduce the dimensionality of a data set.
  
  Improve and simplify machine learning models.
  
  Regression Models
  
  Use supervised learning techniques to perform predictive modeling for continuous response variables.
  
  Neural Networks
  
  Create and train neural networks for clustering and predictive modeling.
  
  Adjust network architecture to improve performance.
Optimiztion Techniques
- Optimization Techniques
  
  Genetic algorithm
  
  Swarm algorithm
  
  Tabu search
  
  Simulated Anneling
  
  Artificial Neural Networks
  
  Support Vector Machines
Image Processing
Renewable Energy
Power Systems
Power Electronics
Electric Machines
Communications
Graphical user Interfaces
Simulink
Python/C/C++/C#/OpenCL/OpenGL

MATLAB Books
Deep Learning
- Deep Learning
  
  Deep Learning for Image Recognition
  
  Object Classification in Photographs
  
  Pretrained Networks
  
  Classifications using a network already created and trained
  
  Identify Objects in Some Images
  
  Making Predictions
  
  CNN Architecture
  
  Investigating Predictions
  
  Image Datastores
  Preprocessing Images
  
  Preparing Images to Use as Input
  
  Augmenting Images in a Datastore
  
  Performing Transfer Learning
  
  Preparing Training Data
  
  Modifying Network Layers
  
  Training Options
  
  Evaluating Performance
  
  Transfer Learning
Machine Learning
- Classification Models
  
  Supervised learning techniques to perform predictive modeling for classification problems.
  
  Predictive Models
  
  Reduce the dimensionality of a data set.
  
  Improve and simplify machine learning models.
  
  Regression Models
  
  Use supervised learning techniques to perform predictive modeling for continuous response variables.
  
  Neural Networks
  
  Create and train neural networks for clustering and predictive modeling.
  
  Adjust network architecture to improve performance.
Optimiztion Techniques
- Optimization Techniques
  
  Genetic algorithm
  
  Swarm algorithm
  
  Tabu search
  
  Simulated Anneling
  
  Artificial Neural Networks
  
  Support Vector Machines
Image Processing
Renewable Energy
Power Systems
Power Electronics
Electric Machines
Communications
Graphical user Interfaces
Simulink
Python/C/C++/C#/OpenCL/OpenGL

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Axis Labels and Title

Execute the following commands on MATLAB before you start: x = 0:0.1:2*pi; y = sin(x); TasksTask 1 Create a line plot of y against x. Use the plot function. The following syntax plots u against t. >> plot(t,u)