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Computer Visionfirst principles.

DEEP LEARNING • FROM NEURONS TO TRANSFORMERS.

Chapter-by-chapter theory notes, step-by-step textbook exercises, video lectures, and SLO-aligned board examination prep for Computer Vision (Deep Learning).

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Syllabus

Curriculum Syllabus

Course chapter sequence roadmap and learning outcomes.

Computer Vision (Deep Learning)

1. Convolutional Layers

1.1 Why CNNs?
1.2 Filters & Feature Maps
1.3 Stride, Padding & Channels
1.4 Pooling Layers

2. CNN Architectures

2.1 LeNet & AlexNet
2.2 VGG & GoogLeNet
2.3 ResNet & Skip Connections
2.4 Efficient & Modern CNNs

3. Vision Tasks

3.1 Image Classification with Pretrained Models
3.2 Object Detection
3.3 Semantic & Instance Segmentation
3.4 Object Tracking

4. Vision Transformers and Modern Vision

4.1 Vision Transformers (ViT)
4.2 Self-Supervised Vision
4.3 Vision-Language Models
INTERACTIVE LAB • 18 STEM WORKSTATIONS

Bridge High-School Science to Computational AI

Connect textbook physics, mathematics, and computing to real-world artificial intelligence models and symbolic solvers.

scienceOpen Interactive Laboratories→Board MCQs & Past Papers