Computer Vision Deep Dive | From Pixels to Vision Transformers | AI Course Day 6 скачать в хорошем качестве

Computer Vision Deep Dive | From Pixels to Vision Transformers | AI Course Day 6

Welcome to Day 6 of “Master AI in 30 Days” — a deep, end-to-end guide to Computer Vision, one of the most powerful and widely used domains of Artificial Intelligence. In this session, you’ll learn how machines see, understand images as numbers, and power real-world systems like self-driving cars, medical imaging, face recognition, satellite analysis, and multimodal AI. This video is designed for students, engineers, and professionals who want a conceptual + system-level understanding of modern computer vision — not just surface-level definitions. 🚀 What You’ll Learn in This Video: How images are represented as matrices of numbers How Convolutional Neural Networks (CNNs) actually work Advanced CNN architectures: ResNet, Inception, EfficientNet Object Detection fundamentals (YOLO, R-CNN, Faster R-CNN) Image Segmentation: Semantic, Instance & Panoptic Vision Transformers (ViT) and why they challenge CNNs Multimodal vision-language models like CLIP Transfer Learning & ImageNet pretraining Real-world applications in autonomous vehicles, healthcare, security, satellites Challenges: robustness, adversarial attacks, efficiency, interpretability The future of vision AI: foundation models, NAS, self-supervised learning This episode builds directly on: ✔ Day 4: Deep Learning ✔ Day 5: Natural Language Processing 👉 Next Episode (Day 7): Reinforcement Learning Basics If you are serious about learning AI from fundamentals to frontier, this series is for you. ⏱️ TIMESTAMPS 00:00 – Introduction & Course Context 01:18 – What is Computer Vision & Why It Matters 02:58 – Images as Numbers (Pixels, RGB, Matrices) 04:27 – CNN Foundations: Convolution & Pooling 06:15 – Advanced CNN Architectures (ResNet, Inception, EfficientNet) 07:00 – Object Detection Explained (YOLO vs R-CNN) 09:47 – Image Segmentation: Semantic vs Instance vs Panoptic 11:44 – Vision Transformers (ViT) Explained 13:29 – Real-World Applications of Computer Vision 15:44 – Multimodal Vision + Language Models (CLIP, VQA) 17:47 – Transfer Learning & ImageNet Pretraining 19:28 – Challenges in Computer Vision Systems 21:19 – Neural Architecture Search (NAS) 23:10 – Future of Computer Vision & Foundation Models 25:09 – What’s Next: Reinforcement Learning (Day 7)