Lecture on Convolutional Neural Networks (CNNs)

Jul 14, 2024

Lecture on Convolutional Neural Networks (CNNs)

Introduction to CNNs

  • Convolutional Neural Networks (CNNs) are a subset of AI reliable for image-related tasks due to their efficiency.
  • Mainly used for tasks like image segmentation, classification, and object detection.

Agenda

  1. Review of Neural Networks
  2. Focus on CNNs
  3. Breakout Room Discussions
  4. Techniques to improve models
  5. Ways to evaluate models.
  6. Hands-on project

Review of Neural Networks

  • Neural Networks (NN): Specialized algorithms that learn from data sets provided to improve performance for specific tasks.
    • Key Elements:
      • Input Layer: Receives the input data.
      • Hidden Layers: Nodes (neurons) that transform data and learn complex patterns.
      • Output Layer: Final output based on the transformations in hidden layers.
    • Basic NNs are typically used for binary classification.

Data Types in NNs

  • Numerical Data: Quantitative data like age, measurements.
  • Categorical Data: Qualitative data like gender, country.
  • Supervised Learning: Learning from labeled datasets.
  • Unsupervised Learning: Learning from raw, unlabeled data, often using clustering.

K-Nearest Neighbors (K-NN)

  • K-NN: Classifies data points based on their k-nearest neighbors.
  • Importance of K: Odd number to prevent ties in classification; too small K is sensitive to noise; too large K overgeneralizes.

Review of Machine Learning & Deep Learning

  • Machine Learning Model: Splits tasks into feature extraction & classification.
  • Deep Learning Model: Handles both feature extraction and classification, learning from complex patterns.
  • Components:
    • Nodes: Receive input signals and perform transformations.
    • Weights & Biases: Influence transformations; weights scale inputs and biases shift functions.
    • Activation Functions: Introduce non-linearity and decide node output.
    • Backpropagation: Algorithms to update weights to minimize loss.

Introduction to CNN

  • Structure of CNN:
    • Convolutional Layers: Apply filters/kernels to detect patterns.
    • Pooling Layers (Max Pooling): Downsample dimensions to reduce computational cost.
    • Dense/Fully Connected Layers: Perform final classifications.
    • Common Tasks: Image segmentation, classification, object detection.
  • Hierarchy of Features: Multiple convolutional and pooling layers help detect detailed features.

Techniques to Improve CNNs

  1. Skip Connections: Pass information across layers to optimize learning from features, common in U-Net models used for medical image segmentation.
  2. Batch Normalization: Normalizes inputs to speed up training and add regularization.
  3. Dropout: Prevents overfitting by randomly dropping nodes during training.
  4. Padding: Preserves spatial dimensions of feature maps.
  5. Regularization Techniques: Such as L1, L2, and adding penalty terms to control weights.

Model Evaluation Techniques

  • Confusion Matrix: Evaluate model precision, recall, accuracy, and F1 score.
  • Area Under the Curve (AUC): Measures model's performance across different thresholds.

Common Errors & Model Degradation

  • Check for issues like dimension mismatching, layers' discrepancies, memory management, and device compatibility.
  • Monitor for signs of model degradation, like drifting data features or abrupt declines in performance.

Hands-On Project Overview

  • Presentation and demonstration of coding a CNN using tools like Google Colab & PyTorch.
  • Tasks included data loading, model creation, forward function definition, training loop, and testing.

Future Topics

  • For the next lecture: deeper dive into coding projects, advanced model techniques, etc.