INFO
A subset of machine learning that focuses on training artificial neural networks to recognize patterns and make complex decisions based on large volumes of data.
Purpose
- Enable models to automatically extract hierarchical features from raw data
- Support generalization across diverse applications
- Handle vast amounts of unstructured data efficiently
How It Works
- Leverages multiple layers of abstraction to learn meaningful representations
- Uses techniques like:
- Transfer Learning: Fine-tune pre-trained models for new tasks
- Self-Supervised Learning: Learn from raw data without external labels
- Scalable across domains with minimal labeled data
Video Resource
Deep Learning Paradigms
Supervised Deep Learning
INFO
Trains on labeled datasets to learn mappings from inputs to known outputs.
Process
- Model learns to associate input features with annotated targets
- Common in classification, regression, and structured prediction tasks
Advantages
- High accuracy when trained on large, well-labeled datasets
Disadvantages
- Requires substantial computational resources
- Needs extensive annotated data for optimal performance
Unsupervised Deep Learning
INFO
Focuses on extracting meaningful patterns and representations from data without explicit labels.
Techniques
- Autoencoders: Learn compressed representations
- GANs: Generate synthetic data and augment datasets
- Dimensionality Reduction: Reveal latent structure
- Data Augmentation: Enhance learning in low-label domains
Reinforced Deep Learning
INFO
Integrates Deep Learning to enable agents to make sequential decisions based on interactions with an environment.
Characteristics
- Learns optimal strategies via trial and error
- Maximizes cumulative rewards over time
Applications
- Robotics
- Game playing
- Autonomous systems
Techniques
- Deep Q-Networks (DQNs): Value-based learning with deep nets
- Policy Gradient Methods: Direct optimization of decision policies
- Handles high-dimensional inputs and complex environments
Hybrid Deep Learning
INFO
Combines neural networks with domain-specific constraints such as differential equations or symbolic rules.
Example
- Physics-Informed Neural Networks (PINNs)
- Embed physical laws into the learning process
- Solve PDEs more efficiently than traditional numerical methods
Applications
- Physics
- Engineering
- Climate modeling
Semi-Supervised Deep Learning
INFO
Combines supervised and unsupervised learning to leverage small amounts of labeled data with large volumes of unlabeled data. Useful when annotation is costly or limited.
Example
- Semi-Supervised Classification
- Initial training on labeled samples
- Refined using unlabeled data via techniques like pseudo-labeling and consistency regularization
Applications
- Healthcare diagnostics
- Natural Language Processing
- Computer Vision
- Fraud detection in financial systems
Self-Supervised Deep Learning
INFO
Operates entirely on unlabeled data by generating pseudo-labels from intrinsic data properties. Enables scalable pretraining without manual annotation.
Example
- Masked Language Modeling (e.g., BERT)
- Predict missing tokens in text
- Learn rich representations for downstream NLP tasks
- Contrastive Learning (e.g., SimCLR)
- Learn embeddings by comparing augmented views of the same data
Applications
- Image classification
- Video analysis
- Language modeling
- Recommendation systems