Demystifying Machine Intelligence: Understand the Language of Tomorrow.
The simulation of human intelligence in machines programmed to think, learn, and problem-solve. Encompasses ML, deep learning, NLP, and computer vision.
A subset of AI where algorithms improve automatically through experience. Systems learn patterns from data without explicit programming.
ML using neural networks with multiple layers. Powers image recognition, NLP, and complex pattern detection. Requires large datasets and GPU compute.
Computing systems inspired by biological neural networks. Composed of interconnected nodes (neurons) organized in layers that process and transmit information.
ML approach using labeled training data. The algorithm learns from input-output pairs to make predictions on new, unseen data.
ML using unlabeled data. Algorithms find hidden patterns, clusters, or structures without predefined categories. Used for anomaly detection and segmentation.
AI technology enabling machines to understand, interpret, and generate human language. Powers chatbots, translation, and text analysis.
AI field enabling machines to interpret visual information from images and videos. Used in facial recognition, object detection, and quality control.
AI systems that create new content (text, images, code) based on training data. Includes GPT models, DALL-E, and other generative models.
Practices for deploying and maintaining ML models in production. Combines ML, DevOps, and data engineering for scalable AI systems.
Neural network architecture using attention mechanisms. Foundation of modern NLP models like GPT, BERT, and LLMs. Revolutionized language understanding.
Centralized repository storing structured and unstructured data at any scale. Essential infrastructure for big data analytics and ML training.