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

Описание: 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)