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**Deep Learning**- Deep Learning for Image Recognition
- Object Classification in Photographs

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- 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

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

-100%

Courses, Deep Learning# Deep Learning for image recognition

Rated **4.72** out of 5 based on 36 customer ratings

(36 customer reviews) Get started quickly using deep learning methods to perform image recognition.

€0 ~~€99~~

MATLAB4Engineers |
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4.7 overall

28

6

2

0

0

5out of 5alaa zakaria–like

alaa zakaria–5out of 5ahmad–very good course

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Alaa Alakkad–3out of 5abdullah sy–good

abdullah sy–5out of 5Asmaa Laila–exellent

Asmaa Laila–5out of 5Asmaa Laila–perfect

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Osama mahmoud–4out of 5mhmd abdoon–good

mhmd abdoon–5out of 5Mohamad al sarakpi–excellent

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mohammad sy–5out of 5Ahmed–Really is a very interesting course. The contents are very helpful for students who do not have any previous experience with deep learning in MATLAB

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Haider–5out of 5Moha Ibra–An interesting topic and the lessons are valuable.

Thanks for this course

Moha Ibra–5out of 5abdalmohaymen–الله يجعلها في ميزان حسناتكم

abdalmohaymen–5out of 5abdalmohaymen–thanks

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samking–5out of 5mohamed samir–this course is very good

mohamed samir–5out of 5Aladdin–An amazing course, I recommend it for everyone interested in Deep learning

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