Your comprehensive, curated guide to understanding Artificial Intelligence. From fundamental concepts to cutting-edge research.
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Artificial Intelligence is the simulation of human intelligence in machines programmed to think and learn. AI systems can perceive their environment, reason about information, make decisions, and adapt their behavior to achieve specific goals across diverse domains.
Deep learning is a subset of machine learning using neural networks with multiple hidden layers to automatically learn hierarchical representations of data. It has revolutionized AI by achieving human-level performance in image recognition, natural language processing, and other complex tasks through end-to-end learning from raw data.
An AI agent is an autonomous system that perceives its environment, maintains internal state, and takes actions to achieve specific goals through a perceive-plan-act-observe cycle. Agents can use tools, interact with external systems, and adapt their behavior based on feedback and changing conditions.
Backpropagation is the fundamental algorithm for training neural networks, using the chain rule of calculus to efficiently compute gradients of the loss function with respect to network parameters. It propagates error information backward through layers, enabling optimization algorithms to adjust weights and biases to minimize prediction errors.
BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based language model developed by Google that revolutionized natural language understanding by using bidirectional context. Unlike autoregressive models like GPT, BERT can attend to both past and future tokens simultaneously, making it exceptionally effective for understanding tasks like question answering, sentiment analysis, and text classification.
A Convolutional Neural Network is a specialized deep learning architecture designed for processing grid-like data such as images. It uses convolutional layers with learnable filters to detect local features, pooling layers to reduce dimensionality, and hierarchical feature extraction to achieve state-of-the-art performance in computer vision tasks.
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