Using Statistical Operations on Matrices
Motivation Demonstration
- Deep Learning
- Deep Learning for Image Recognition
- Object Classification in Photographs
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- MATLAB Books
- Deep Learning
- 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
Youtube
- 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