Deep Learning for Computer Vision Applications
( 25 Modules )

Module #1
Introduction to Computer Vision
Overview of computer vision, its applications, and the role of deep learning

Module #2
Mathematical Preliminaries
Review of linear algebra, calculus, and probability theory for deep learning

Module #3
Introduction to Deep Learning
Basic concepts of deep learning, including neural networks, perceptrons, and backpropagation

Module #4
Convolutional Neural Networks (CNNs)
Architecture and applications of CNNs, including convolutional and pooling layers

Module #5
CNN Architectures
Overview of popular CNN architectures, including LeNet, AlexNet, VGGNet, and ResNet

Module #6
Image Classification
Applications of CNNs to image classification tasks, including dataset preparation and evaluation metrics

Module #7
Object Detection
Introduction to object detection, including sliding window, R-CNN, and YOLO algorithms

Module #8
Semantic Segmentation
Overview of semantic segmentation, including FCN, U-Net, and SegNet architectures

Module #9
Image Processing and Augmentation
Techniques for image preprocessing, data augmentation, and feature engineering

Module #10
Deep Learning Frameworks
Introduction to popular deep learning frameworks, including TensorFlow, PyTorch, and Keras

Module #11
Building and Training CNNs
Hands-on experience building and training CNNs using a deep learning framework

Module #12
Transfer Learning and Fine-tuning
Using pre-trained models and fine-tuning for computer vision tasks

Module #13
Object Tracking
Algorithms and techniques for object tracking, including Kalman filter and particle filter

Module #14
Scene Understanding
Introduction to scene understanding, including layout estimation, and 3D reconstruction

Module #15
Action Recognition
Overview of action recognition, including dataset and evaluation metrics

Module #16
Generative Models for Computer Vision
Introduction to generative models, including GANs, VAEs, and style transfer

Module #17
Deep Learning for Video Analysis
Applications of deep learning to video analysis, including object detection and tracking

Module #18
Deep Learning for 3D Vision
Introduction to deep learning for 3D vision, including 3D reconstruction and point cloud processing

Module #19
Deep Learning for Medical Imaging
Applications of deep learning to medical imaging, including image segmentation and disease diagnosis

Module #20
Deep Learning for Autonomous Vehicles
Overview of deep learning for autonomous vehicles, including object detection, tracking, and motion forecasting

Module #21
Deep Learning for Surveillance
Applications of deep learning to surveillance, including object detection, tracking, and behavior analysis

Module #22
Deep Learning for Robotics
Introduction to deep learning for robotics, including visual SLAM and grasping

Module #23
Deep Learning for Facial Analysis
Overview of deep learning for facial analysis, including face recognition, detection, and emotion recognition

Module #24
Deep Learning for Natural Language Processing
Introduction to deep learning for natural language processing, including text-to-image synthesis

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Deep Learning for Computer Vision Applications career

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Deep Learning for Computer Vision Applications ( 25 Modules )

Deep Learning for Computer Vision Applications
( 25 Modules )