🖼️ Convolutional Neural Networks

Master CNNs for computer vision, image processing, and deep learning applications with spatial data

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Convolutional Neural Networks Curriculum

12
Core Units
~80
Key Concepts
15+
CNN Architectures
35+
Practical Examples
1

Introduction to Computer Vision

Understand the challenges of computer vision and why CNNs are essential.

  • Computer vision tasks
  • Image representation
  • Pixel values and channels
  • Image classification challenges
  • Traditional vs deep learning
  • CNN motivation
  • Spatial hierarchy
  • Translation invariance
2

Convolution Operation

Master the fundamental convolution operation and its mathematical properties.

  • Convolution mathematics
  • Kernel/filter concept
  • Stride and padding
  • Feature detection
  • Edge detection examples
  • Output size calculation
  • Multiple filters
  • Convolution vs correlation
3

Convolutional Layers

Build convolutional layers and understand feature map generation.

  • Conv2D layer structure
  • Feature maps
  • Parameter sharing
  • Local connectivity
  • Depth and channels
  • Receptive fields
  • Layer parameters
  • Computational efficiency
4

Pooling Layers

Learn pooling operations for dimensionality reduction and translation invariance.

  • Pooling motivation
  • Max pooling
  • Average pooling
  • Global pooling
  • Spatial downsampling
  • Translation invariance
  • Pooling vs convolution
  • Modern alternatives
5

CNN Architecture Design

Design complete CNN architectures for image classification tasks.

  • Layer stacking principles
  • Feature hierarchy
  • Spatial resolution reduction
  • Channel depth increase
  • Fully connected layers
  • Output layer design
  • Architecture patterns
  • Design considerations
6

Classic CNN Architectures

Study landmark CNN architectures that shaped computer vision.

  • LeNet-5 architecture
  • AlexNet breakthrough
  • VGGNet design
  • GoogLeNet/Inception
  • ResNet innovations
  • DenseNet connections
  • MobileNet efficiency
  • EfficientNet scaling
7

Transfer Learning

Leverage pre-trained models for efficient learning on new tasks.

  • Transfer learning concept
  • Feature extraction
  • Fine-tuning strategies
  • Layer freezing
  • Pre-trained models
  • Domain adaptation
  • Data requirements
  • Best practices
8

Data Augmentation

Improve model generalization through data augmentation techniques.

  • Augmentation importance
  • Geometric transformations
  • Color space changes
  • Noise injection
  • Cutout and mixup
  • Auto augmentation
  • Implementation strategies
  • Augmentation policies
9

Object Detection

Extend CNNs to locate and classify objects within images.

  • Object detection tasks
  • Bounding box regression
  • R-CNN family
  • YOLO architecture
  • SSD networks
  • Anchor boxes
  • Non-max suppression
  • Evaluation metrics
10

Semantic Segmentation

Perform pixel-level classification for detailed scene understanding.

  • Segmentation vs classification
  • Fully Convolutional Networks
  • U-Net architecture
  • Skip connections
  • Dilated convolutions
  • DeepLab models
  • Loss functions
  • Evaluation metrics
11

Advanced CNN Techniques

Explore advanced techniques for improving CNN performance.

  • Batch normalization
  • Dropout in CNNs
  • Activation functions
  • Skip connections
  • Attention mechanisms
  • Depthwise convolutions
  • Group convolutions
  • Neural architecture search
12

CNN Implementation

Build and deploy CNN models using modern frameworks and tools.

  • TensorFlow/Keras implementation
  • PyTorch CNNs
  • Model optimization
  • GPU acceleration
  • Model deployment
  • Edge deployment
  • Performance tuning
  • Real-world projects

Unit 1: Introduction to Computer Vision

Understand the challenges of computer vision and why CNNs are essential.

Computer Vision Tasks

Explore the various tasks that computer vision systems can perform.

Classification Detection Segmentation
Computer vision encompasses tasks like image classification, object detection, semantic segmentation, face recognition, medical imaging, and autonomous driving - all requiring understanding of visual patterns.

Image Representation

Learn how images are represented as numerical data for computer processing.

Digital Images: 2D/3D arrays of pixel values
Grayscale: Single channel with intensity values 0-255
Color (RGB): Three channels (Red, Green, Blue)
Shape: (Height, Width, Channels) for most frameworks
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

def demonstrate_image_representation():
  """Show how images are represented as arrays"""
  
  print("=== IMAGE REPRESENTATION DEMO ===")
  
  # Create a simple 8x8 grayscale image
  simple_image = np.array([
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 255, 255, 0, 0, 255, 255, 0],
    [0, 255, 255, 0, 0, 255, 255, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 255, 255, 0, 0, 0],
    [0, 0, 255, 0, 0, 255, 0, 0],
    [0, 0, 0, 255, 255, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0]
  ], dtype=np.uint8)
  
  print(f"Image shape: {simple_image.shape}")
  print(f"Data type: {simple_image.dtype}")
  print(f"Min pixel value: {simple_image.min()}")
  print(f"Max pixel value: {simple_image.max()}")
  
  # Create a color (RGB) version
  color_image = np.zeros((8, 8, 3), dtype=np.uint8)
  color_image[:,:,0] = simple_image # Red channel
  color_image[:,:,1] = simple_image // 2 # Green channel
  color_image[:,:,2] = simple_image // 4 # Blue channel
  
  print(f"\\nColor image shape: {color_image.shape}")
  print(f"Number of channels: {color_image.shape[2]}")
  
  # Show pixel values for a small region
  print("\\n=== PIXEL VALUES (top-left 4x4) ===")
  print("Grayscale:")
  print(simple_image[:4, :4])
  
  print("\\nRGB (Red channel):")
  print(color_image[:4, :4, 0])
  
  return simple_image, color_image

# Run demonstration
gray_img, color_img = demonstrate_image_representation()

print("\\n=== KEY CONCEPTS ===")
concepts = {
  "Pixels": "Basic unit of digital images (picture elements)",
  "Channels": "Color components (1 for grayscale, 3 for RGB)",
  "Resolution": "Image dimensions (width x height)",
  "Bit Depth": "Number of bits per pixel (8-bit = 256 levels)",
  "Arrays": "Images stored as multi-dimensional arrays",
  "Normalization": "Often scale pixels to [0,1] range for neural networks"
}

for concept, description in concepts.items():
  print(f"{concept}: {description}")

Image Classification Challenges

Understand the fundamental challenges that make computer vision difficult.

Viewpoint Variation: Objects look different from different angles
Scale Variation: Objects appear at different sizes
Illumination: Lighting conditions affect appearance
Occlusion: Objects may be partially hidden
Intra-class Variation: Objects in same class look different
# Computer vision challenges demonstration

def demonstrate_cv_challenges():
  """Illustrate challenges in computer vision"""
  
  print("=== COMPUTER VISION CHALLENGES ===")
  
  challenges = {
    "Viewpoint Variation": {
      "Problem": "Same object looks different from different angles",
      "Example": "A car from front vs side vs back view",
      "CNN Solution": "Translation and rotation invariance through pooling"
    },
    "Scale Variation": {
      "Problem": "Objects appear at different sizes",
      "Example": "Cat close-up vs cat far away",
      "CNN Solution": "Multi-scale feature detection with different filter sizes"
    },
    "Illumination Changes": {
      "Problem":