Deep Learning
A type of machine learning using neural networks with multiple layers to learn complex patterns from large datasets.
Detailed Definition
Deep Learning is a specialized form of machine learning that uses artificial neural networks with multiple layers (hence "deep") to learn complex patterns from large amounts of data.
Neural network architecture: - Input layer receives data - Hidden layers extract features - Output layer produces predictions - Trained through backpropagation
Advantages of deep learning: - Automatically learns features from raw data - Handles unstructured data (images, text) - Scales with more data and computation - Achieves state-of-the-art accuracy
GIS and mining applications
Image analysis: - Satellite imagery interpretation - Core sample analysis - Geological feature detection - Change detection over time
Document processing: - Handwritten text recognition - Map digitization - Plat interpretation - Signature verification
Geological modeling: - 3D ore body modeling - Lithology prediction from drilling data - Geochemical pattern recognition
Limitations: - Requires large training datasets - Computationally intensive - Less interpretable than traditional methods - May require specialized hardware (GPUs)
Deep learning is particularly valuable for processing imagery and documents in mining and GIS applications.
Related Terms
Artificial Intelligence
Computer systems designed to perform tasks that typically require human intelligence, such as pattern recognition and decision-making.
Machine Learning
A subset of AI where algorithms learn patterns from data to make predictions or decisions without explicit programming.
Computer Vision
AI technology that enables computers to interpret and analyze visual information from images and videos.