This document discusses deep learning MATLAB projects and research areas. It outlines fundamental deep learning topics like faster feature learning and scalability. New research areas mentioned include satellite imaging, medical imaging, and communication. Deep learning can be applied to tasks like vegetation identification, disease diagnosis, and wireless security. The document also lists MATLAB toolboxes and functions for deep learning like Augument, SqueezeNet, MaxPooling2dLayer, Convolutional2dLayer, and training options. It provides contact information for more details on deep learning MATLAB projects.

1. DEEP LEARNING
MATLAB PROJCTS
www.matlabsimulation.com/deep-learning-matlab-projects/

2. Fundamental Topics – Deep Learning
The following developments of key based application fields are below,
High reusable and
cardinality
Faster feature learning
Highly scalable and
generalizable
Robustness to variations
Massive parallel
computation
Easy detection of complex
interaction

3. New Research Areas - Deep Learning Projects
Satellite
Imaging
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Medical
Imaging
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Communication
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• Green Vegetation area identification
• High resolution streak detection
• Disaster mitigation planning and recovery
• Plant disease diagnosis
• 3D surface reconstruction
• Tumor segmentation and also interpretation
• Parallel inter image registration
• Multi-Modality based Motion analysis
• Navigation and tracking
• Green prevasive computing
• Control devices and instruments
• Wireless security and routing
The following outline of deep learning matlab research areas are listed below,

4. Toolbox - Deep Learning Matlab Projects
The persistence for deep learning matlab toolboxes are listed below,
Augument () and
layerGraph ()
SqueezeNet ()
and also
GoogleNet ()
MaxPooling2dLayer
() and 3dLayer ()
Convolutional2d
layer() and 3dlayer ()
Training options
() and
TrainNetwork ()
ReLulayer () and
LeakyReLu layer ()

5. Contact Us
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www.matlabsimulation.com
+91 9444856435
matlabguide@gmail.com