Modern Computer Vision & Deep Learning with Python & PyTorch

🚀 Course Overview:

Welcome to the comprehensive course on "Deep Learning for Computer Vision with Python and PyTorch"! This course is meticulously crafted to take you from the foundational concepts of Deep Learning, through advanced techniques in Image Classification, Semantic Segmentation, Object Detection, and Instance Segmentation, using Python and PyTorch.

🎓 What You Will Learn:

1. Understand the basics of Neural Networks and Convolutional Neural Networks (CNNs).
2. Explore state-of-the-art Deep Learning architectures for Image Classification like VGG, ResNet, and Inception.
3. FineTune the Deep Resnet Model and learn how to use it as a fixed feature extractor.
4. Dive into Hyperparameters Optimization and Results Visualization.
5. Learn about Semantic Image Segmentation, its real-world applications like self-driving cars, and how PyTorch facilitates this process.
6. Explore Deep Learning Architectures for Semantic Segmentation, including PSPNet, UNet, UNet++, PAN, MTCNet, DeepLabV3, etc.
7. Discover tools for Datasets and Data Annotations for Semantic Segmentation.
8. Master Data Augmentation and Data Loading in PyTorch for Semantic Segmentation.
9. Understand Performance Metrics (IOU) for Semantic Segmentation Models Evaluation.
10. Learn Transfer Learning using Pretrained Deep Resnet Architectures.
11. Implement various Segmentation Models in PyTorch with different Encoder and Decoder Architectures.
12. Train and test your own Segmentation Models, calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall, and F-score.
13. Visualize Segmentation Results and generate RGB Predicted Segmentation Map.
14. Learn about Object Detection using Deep Learning Models with PyTorch.
15. Study RCNN, Fast RCNN, Faster RCNN, and Mask RCNN architectures.
16. Perform Object Detection with Fast RCNN and Faster RCNN.
17. Get introduced to Detectron2 by Facebook AI Research (FAIR) for Object Detection tasks.
18. Apply Detectron2 models to your own datasets.
19. Explore Custom Object Detection Datasets with Annotations.
20. Train, Test, Evaluate your Own Object Detection Models and Visualize Results.
21. Perform Instance Segmentation using Mask RCNN on Custom Dataset with Pytorch and Python.

👨‍💼👩‍💻 Who Should Attend:

This course is designed for a broad audience, including:

• Computer Vision Engineers and AI Enthusiasts
• Machine Learning Engineers and Deep Learning Engineers
• Data Scientists and Developers
• Graduates and Researchers in Computer Science, Electrical Engineering, and related fields
• Anyone interested in learning about the latest advances in Deep Learning for Computer Vision using Python and PyTorch

🌟 What's Inside:

This course is a journey into the depths of computer vision and deep learning. It's not just about theory; you'll get hands-on experience with real-world datasets, model training, evaluation metrics, and practical applications. You'll learn to implement state-of-the-art models, optimize them for peak performance, and apply them to solve complex problems in image classification, segmentation, and object detection.

🔍 Course Highlights:

• Learn from the ground up, with clear explanations of each concept.
• Gain hands-on experience by working on real-world projects.
• Access to high-quality resources, including code, datasets, and reference materials.
• Engage with an active community. 🔥 Your Next Step:

Embark on this transformative journey of Deep Learning for Computer Vision with Python and PyTorch. By the end of this course, you will be well-equipped to tackle complex problems in your work or research using Deep Learning techniques. Whether you are a beginner or an experienced professional, this course aims to elevate your expertise in the field of computer vision and deep learning using Python and PyTorch. 🔥 Let's Get Started! 🚀

Join us on this exciting journey of mastering Deep Learning for Computer Vision with Python and PyTorCH. Let's dive into the world of deep learning, image processing, and beyond, together we can make a significant impact in the field of computer vision. See you inside the Class!! �✨