Deep Learning for Computer Vision
( 24 Modules )

Module #1
Introduction to Computer Vision
Overview of computer vision, its applications, and importance of deep learning in CV

Module #2
Deep Learning Fundamentals
Introduction to deep learning, neural networks, and key concepts

Module #3
Convolutional Neural Networks (CNNs) Basics
Introduction to CNNs, architecture, and advantages

Module #4
Image Preprocessing and Augmentation
Importance of preprocessing, data augmentation techniques, and tools

Module #5
Building and Training a CNN
Hands-on experience building and training a CNN using popular libraries

Module #6
Activation Functions and Optimization Techniques
Understanding activation functions, optimization algorithms, and their roles

Module #7
Convolutional Layers and Pooling
In-depth understanding of convolutional layers and pooling techniques

Module #8
Flattening, Dense Layers, and Outputs
Understanding flattening, dense layers, and output layers in CNNs

Module #9
Transfer Learning and Fine-tuning
Leveraging pre-trained models, transfer learning, and fine-tuning techniques

Module #10
Object Detection Fundamentals
Introduction to object detection, its applications, and challenges

Module #11
Yolo (You Only Look Once) Algorithm
In-depth explanation of the Yolo algorithm and its variants

Module #12
SSD (Single Shot Detector) Algorithm
In-depth explanation of the SSD algorithm and its variants

Module #13
Segmentation Techniques
Introduction to image segmentation, types, and applications

Module #14
U-Net and Fully Convolutional Networks (FCNs)
In-depth explanation of U-Net and FCNs for image segmentation

Module #15
Generative Adversarial Networks (GANs) for CV
Introduction to GANs, its applications, and limitations in CV

Module #16
Deep Learning for Image Classification
Advanced techniques for image classification using deep learning

Module #17
Deep Learning for Image Retrieval
Introduction to image retrieval, its applications, and deep learning approaches

Module #18
Deep Learning for Video Analysis
Introduction to video analysis, its applications, and deep learning approaches

Module #19
Attention Mechanisms in CV
In-depth explanation of attention mechanisms and their applications in CV

Module #20
Explainability and Interpretability in CV
Importance of explainability and interpretability in CV, techniques, and tools

Module #21
CV Applications in Healthcare
Deep learning applications in healthcare, medical imaging, and diagnosis

Module #22
CV Applications in Autonomous Systems
Deep learning applications in autonomous systems, self-driving cars, and robotics

Module #23
CV Applications in Surveillance and Security
Deep learning applications in surveillance, security, and monitoring systems

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Deep Learning for Computer Vision career

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Deep Learning for Computer Vision ( 24 Modules )

Deep Learning for Computer Vision
( 24 Modules )