Neural Networks in Machine Learning

Machine Learning

Neural Networks

• A neural network is a machine learning model inspired by the structure and function of the human brain.
• It consists of layers of interconnected nodes (neurons) that process and transmit information.
• Types of neural networks:
  • Feedforward neural networks: information flows only in one direction, from input layer to output layer.
  • Recurrent neural networks (RNNs): information can flow in a loop, allowing the network to keep track of state.
  • Convolutional neural networks (CNNs): specialized for image and signal processing.

Supervised Learning

• A type of machine learning where the model is trained on labeled data.
• The goal is to learn a mapping between input data and output labels.
• Types of supervised learning:
  • Regression: predict a continuous value (e.g. price of a house).
  • Classification: predict a categorical label (e.g. spam/not spam emails).

Unsupervised Learning

• A type of machine learning where the model is trained on unlabeled data.
• The goal is to discover patterns or structure in the data.
• Types of unsupervised learning:
  • Clustering: group similar data points into clusters.
  • Dimensionality reduction: reduce the number of features in the data while preserving important information.

Deep Learning

• A subfield of machine learning that focuses on neural networks with multiple layers.
• Deep learning models are particularly effective for tasks such as:
  • Image recognition
  • Speech recognition
  • Natural language processing

Natural Language Processing (NLP)

• A subfield of artificial intelligence that deals with the interaction between computers and human language.
• NLP tasks include:
  • Text classification (e.g. sentiment analysis)
  • Language translation
  • Named entity recognition (e.g. extracting names and locations from text)
• NLP techniques:
  • Tokenization: breaking text into individual words or tokens.
  • Part-of-speech tagging: identifying the grammatical category of each word.
  • Dependency parsing: analyzing the grammatical structure of a sentence.

Neural Networks

• Inspired by the structure and function of the human brain
• Consists of layers of interconnected nodes (neurons) that process and transmit information
• Three main types:

  Feedforward Neural Networks

  • Information flows only in one direction, from input layer to output layer

  Recurrent Neural Networks (RNNs)

  • Information can flow in a loop, allowing the network to keep track of state

  Convolutional Neural Networks (CNNs)

  • Specialized for image and signal processing

Supervised Learning

• Trained on labeled data
• Goal is to learn a mapping between input data and output labels
• Two main types:

  Regression

  • Predict a continuous value (e.g. price of a house)

  Classification

  • Predict a categorical label (e.g. spam/not spam emails)

Unsupervised Learning

• Trained on unlabeled data
• Goal is to discover patterns or structure in the data
• Two main types:

  Clustering

  • Group similar data points into clusters

  Dimensionality Reduction

  • Reduce the number of features in the data while preserving important information

Deep Learning

• Subfield of machine learning that focuses on neural networks with multiple layers
• Particularly effective for tasks such as:
  • Image recognition
  • Speech recognition
  • Natural language processing

Natural Language Processing (NLP)

• Subfield of artificial intelligence that deals with the interaction between computers and human language
• Tasks include:
  • Text classification (e.g. sentiment analysis)
  • Language translation
  • Named entity recognition (e.g. extracting names and locations from text)
• Techniques:
  • Tokenization
    • Breaking text into individual words or tokens
  • Part-of-speech tagging
    • Identifying the grammatical category of each word
  • Dependency parsing
    • Analyzing the grammatical structure of a sentence