Image Processing, NLP, and Neural Networks

Image Processing

• Definition: Altering or manipulating digital images using mathematical algorithms to enhance, analyze, or extract information.
• Applications:
  • Image recognition and classification
  • Object detection and tracking
  • Image segmentation and feature extraction
  • Image restoration and enhancement
• Techniques:
  • Filtering (e.g., convolutional filters)
  • Thresholding
  • Edge detection
  • Morphological operations
• AI and ML applications:
  • Facial recognition
  • Object detection in self-driving cars
  • Medical image analysis

LLMS (Limited Memory Limited Slope)

• Definition: A type of neural network activation function that combines the benefits of the sigmoid and ReLU functions.
• Characteristics:
  • Similar to sigmoid for negative inputs
  • Similar to ReLU for positive inputs
  • Has a limited range of outputs
• Advantages:
  • Fast computation
  • Easy to compute derivatives
  • Can be used with stochastic gradient descent
• Applications:
  • Natural Language Processing (NLP)
  • Computer Vision

NLP (Natural Language Processing)

• Definition: The intersection of computer science, artificial intelligence, and linguistics focused on human-computer interaction.
• Goals:
  • Language understanding
  • Language generation
  • Sentiment analysis
  • Text classification
• Techniques:
  • Tokenization
  • Named Entity Recognition (NER)
  • Part-of-Speech (POS) tagging
  • Dependency parsing
• Applications:
  • Chatbots and virtual assistants
  • Sentiment analysis for customer feedback
  • Language translation
  • Text summarization