Basics of Deep Learning and Neural Networks

What is Deep Learning?

• Definition: Subset of machine learning involving algorithms inspired by the structure and function of the human brain, known as artificial neural networks (ANNs).
• Key Concept: Learns from large amounts of data to recognize patterns and make decisions without explicit programming.

Neural Networks

• Neurons: Basic units that process and transmit information in artificial neural networks.
• Layers: Networks organized in layers (input, hidden, output) where data flows through interconnected nodes (neurons).
• Activation Function: Non-linear function that determines if a neuron should be activated based on input.

Training a Neural Network

• Training Data: Input-output pairs used to teach the network to make predictions.
• Loss Function: Measures how well the network's predictions match the actual outputs.
• Optimization: Adjusts network parameters (weights and biases) to minimize the loss using algorithms like gradient descent.

Deep Learning Applications

• Image Classification: Assigning labels to images based on their content (e.g., identifying land cover types in satellite imagery).
• Object Detection: Identifying and locating objects within images (e.g., detecting buildings or vehicles in aerial photographs).
• Segmentation: Partitioning images into meaningful segments for detailed analysis (e.g., delineating land use boundaries).

Tools and Frameworks

• TensorFlow: Open-source library for building and deploying machine learning models, including deep learning.
• PyTorch: Deep learning framework known for its flexibility and ease of use, widely used in research and production.