Deep dive into AI

Deep Dive into AI

  • Presenter: Piyush Kumar Arya

AI Learning Methods

  • AI can learn in different ways based on data and feedback. Three major learning methods are:

    • Supervised Learning:

      • Learns from labeled data.

    • Unsupervised Learning:

      • Finds patterns in unlabeled data.

    • Reinforcement Learning:

      • Learns by interacting with an environment.

Supervised vs Unsupervised Learning

Supervised Learning

  • Uses labeled data

  • Learns from correct answers

  • Common Algorithms:

    • Decision Trees

    • Support Vector Machines (SVM)

    • Neural Networks

  • Examples:

    • Spam detection (emails labeled spam or not spam)

    • Image classification (cat vs. dog photos)

Unsupervised Learning

  • Uses unlabeled data

  • Finds patterns on its own

  • Common Algorithms:

    • Clustering (K-Means)

    • Principal Component Analysis (PCA)

    • Autoencoders

  • Examples:

    • Customer segmentation in marketing

    • Anomaly detection in fraud detection

Reinforcement Learning (RL)

  • AI learns by trial and error, improving based on rewards and punishments.

Key Components:

  • Agent: The AI making decisions.

  • Environment: Where the agent interacts.

  • Reward System: Encourages good actions, penalizes bad ones.

Examples:

  • Self-driving cars adjusting speed and turns

  • AlphaGo (AI beating human Go players)

  • Robotics (robots learning to walk)

Popular Algorithms:

  • Q-Learning

  • Deep Q Networks (DQN)

  • Policy Gradients

Reinforcement Learning Process

  1. Observe: The agent observes the environment.

    • e.g., "Ouch! -50 points"

  2. Select action: The agent chooses an action using a policy.

  3. Action! The agent takes an action.

  4. Get reward or penalty: The agent receives feedback (reward or penalty).

    • e.g., "bad! Next time avoid it."

  5. Update policy: A learning step where the agent updates its policy based on feedback.

  6. Iterate until an optimal policy is found.

Deep Learning & Neural Networks

  • Deep Learning is a subset of Machine Learning using artificial neural networks inspired by the human brain.

Neural Network Structure:

  • Input Layer: Takes in data.

  • Hidden Layers: Processes and learns patterns.

  • Output Layer: Provides final decision/prediction.

Applications and Popular Architectures

  • Computer Vision:

    • Image recognition

    • Facial recognition

  • Natural Language Processing (NLP):

    • Chatbots

    • Translation (Google Translate)

  • Self-Driving Cars:

    • Object detection and decision-making

Popular Architectures:

  • Convolutional Neural Networks (CNNs) for images

  • Recurrent Neural Networks (RNNs) for sequential data

  • Transformers for NLP

Thank You!