Overview of Deep Learning Concepts

Sep 18, 2024

Deep Learning Course Overview

Introduction to Deep Learning

  • Deep learning is a subset of machine learning, which is a subset of artificial intelligence.
  • It has led to many breakthroughs such as:
    • AlphaGo defeating world champion Go players.
    • IBM's Deep Blue defeating Garry Kasparov in chess.
    • IBM's Watson winning Jeopardy!
    • Applications in autonomous vehicles, cancer diagnosis, and fake news detection.

Course Structure

  • Learn how to implement deep learning algorithms in Python.
  • Topics include:
    • Neural networks
    • Training processes: supervised, unsupervised, reinforcement learning
    • Loss functions and optimizers
    • Gradient descent algorithm
    • Neural network architectures

What is Deep Learning?

  • Deep learning learns representations from data through neural networks.
  • Machine Learning vs. Deep Learning:
    • Machine Learning: Recognizes patterns; requires domain expertise.
    • Deep Learning: Learns features from raw data without manual intervention.

Key Historical Achievements

  • 1997: Deep Blue defeats Kasparov in chess.
  • 2011: Watson wins Jeopardy!
  • 2015: AlphaGo defeats Lee Sedol in Go.

Understanding Neural Networks

  • Fundamental components:
    • Input layer, hidden layers, output layer.
    • Neurons process input through weighted connections and activation functions.

Forward and Backpropagation

  • Forward Propagation:

    • Information moves from input to output through layers.
    • Applies weights and biases to inputs.

  • Backpropagation:

    • Adjusts weights and biases based on the error of predictions.
    • Uses loss functions to quantify deviations from expected outputs.

Key Terminologies

  • Weight: Importance of a neuron in the relationship; higher values indicate higher importance.
  • Bias: Shifts the activation function; represents an offset that assists in learning.
  • Activation Function: Introduces non-linearity and decides neuron activation.
    • Common functions: Sigmoid, Tanh, ReLU, Leaky ReLU.

Types of Learning in Deep Learning

Supervised Learning

  • Trains on labeled data; predicts output based on input features.
  • Can be divided into:
    • Classification: Assigns labels (e.g., spam detection).
    • Regression: Predicts continuous values (e.g., housing prices).

Unsupervised Learning

  • Works with unlabeled data to find patterns.
  • Types:
    • Clustering: Groups similar data points (e.g., customer segmentation).
    • Association: Finds relationships between data points (e.g., market basket analysis).

Reinforcement Learning

  • Learns through trial and error using feedback (rewards/punishments).
  • Example: Training an agent in game environments (e.g., Pac-Man).

Overfitting and Regularization

  • Overfitting: When a model performs well on training data but poorly on unseen data.
  • Solutions:
    • Dropout: Randomly drops neurons during training to promote generalization.
    • Data Augmentation: Creates synthetic data by transforming existing data.
    • Early Stopping: Monitors validation loss and stops training to prevent overfitting.
    • Weight Regularization: Constrains weight values to avoid complexity.

Neural Network Architectures

Fully Connected Feedforward Neural Networks

  • Each neuron in one layer connects to every neuron in the next layer.

Convolutional Neural Networks (CNNs)

  • Specializes in image processing tasks.
  • Uses convolutional layers to extract features and pooling layers to reduce dimensionality.

Recurrent Neural Networks (RNNs)

  • Designed for sequential data; retains memory of past states.
  • Variants include Gated RNNs and Long Short-Term Memory (LSTM) networks to combat short-term memory issues.

Steps in Building a Deep Learning Project

  1. Gathering Data: Quality and quantity are crucial.
  2. Pre-processing Data: Splitting into training, validation, and testing sets.
  3. Training the Model: Forward propagation, loss calculation, backpropagation adjustments.
  4. Evaluating Model Performance: Testing on unseen data.
  5. Optimizing Model: Tuning hyperparameters and regularization methods.

Key Takeaways

  • Experimenting and iterating are essential in deep learning.
  • Choosing the right architecture and techniques depends on the problem at hand.
  • Stay updated with recent advancements and practices in the field.