Deep Learning: Introduction and Key Concepts

Jul 10, 2024

Deep Learning: Introduction and Key Concepts

Introduction

  • Deep Learning Importance
    • Revolutionizing many fields, achieving milestones previously thought impossible.
    • Examples include DeepMind's AlphaGo, cancer diagnosis, web translation, autonomous vehicles.

Course Overview

  • Core Topics:
    • Definition and distinction between artificial intelligence, machine learning, and deep learning.
    • Introduction to neural networks and their importance in deep learning.
    • Training deep learning models and the types of learning: supervised, unsupervised, reinforcement learning.
    • Key concepts: loss functions, optimizers, gradient descent, neural network architectures.

Historical Milestones in Deep Learning

  • 1997: IBM's Deep Blue defeats chess champion Gary Kasparov.
  • 2011: IBM's Watson wins Jeopardy against top players.
  • 2015: Google's AlphaGo beats world champion Lee Sedol at Go.
  • Applications: Self-driving cars, fake news detection, earthquake prediction.

Fundamentals of Deep Learning

  • Definition: Subset of Machine Learning (ML), part of Artificial Intelligence (AI).
  • Machine Learning: Algorithms teach computers to recognize patterns in data similar to how humans do.
  • Challenges: Teaching machines to distinguish between objects like cats and dogs.

Neural Networks

  • Architecture: Layers of neurons, including input layer, hidden layers, output layer.
  • Learning Process: Forward propagation and back propagation.
    • Forward Propagation: Input processed through layers to generate output.
    • Back Propagation: Adjusts weights and biases to minimize error using loss function.
  • Training: Training involves iteratively adjusting weights and biases to reduce prediction error.
  • Example: Predicting vehicle types using neural networks adjusting from input weights, goods carried, to final classification.

Key Concepts

  • Activation Functions
    • Introduces non-linearity in network, allows modeling of complex functions.
    • Types: Step Function, Linear Function, Sigmoid, TanH, ReLU, Leaky ReLU.
    • Sigmoid: Outputs between 0-1 but can cause vanishing gradient problem.
    • TanH: Similar to sigmoid but ranges from -1 to 1.
    • ReLU: Outputs value or 0, efficient but can lead to 'dying ReLU' problem.
  • Loss Functions: Quantifies difference between predicted and actual output (e.g., squared error loss, cross-entropy).
  • Optimizers
    • Gradient Descent: Minimizes loss function iteratively by adjusting weights.
    • Variants: Stochastic Gradient Descent, AdaGrad, RMSProp, Adam.
    • Learning Rate: Controls step size in gradient descent.
  • Model Parameters vs Hyperparameters
    • Parameters: Internal values (weights, biases) estimated from data.
    • Hyperparameters: External configurations set manually (learning rate, epoch number).
  • Epochs, Batch Size, Iterations
    • Epoch: One pass through the entire dataset.
    • Batch: Subset of data processed in one step.
    • Iterations: Number of batches per epoch.

Types of Learning

  • Supervised Learning
    • Training with labeled data to map input to output (e.g., classification and regression).
  • Unsupervised Learning
    • Finding patterns in unlabeled data (e.g., clustering, association).
  • Reinforcement Learning
    • Learning through rewards and punishments to maximize overall reward.

Preventing Overfitting

  • Overfitting: Model performs well on training data but poorly on new data.
  • Regularization Techniques
    • Dropout: Randomly dropping neurons during training to prevent co-dependency.
    • Data Augmentation: Generating new data from existing data to enhance training set.
    • Early Stopping: Halting training when validation error begins to rise.

Neural Network Architectures

  • Feed-forward Networks: Simple form, no cycles, each neuron connected to next layer.
  • Recurrent Neural Networks (RNNs): For sequence data, includes feedback loops to remember past information (e.g., text prediction).
    • Challenges: Short-term memory due to vanishing gradient.
    • Variants: LSTM (Long Short-Term Memory), Gated RNNs.
  • Convolutional Neural Networks (CNNs): For image data, includes convolutional and pooling layers to reduce dimensionality and extract features.
    • Applications: Image recognition, segmentation, video analysis.

Practical Steps in Deep Learning Projects

  1. Data Collection: Gather sufficient and high-quality data relevant to problem.
  2. Data Preprocessing:
    • Splitting data into training, validation, and test sets.
    • Handling missing data and imbalanced data.
    • Feature scaling and normalization.
  3. Model Training: With chosen architecture, adjusting through back propagation and evaluating with validation set.
  4. Model Evaluation: Testing against unseen data to check for accuracy and generalization.
  5. Optimization: Tuning hyperparameters, regularization techniques to improve performance.