
Explore the fundamentals of artificial intelligence, from the Turing test to weak, strong, and super AI, and how AI blends computer science, mathematics, neuroscience, and psychology.
Explore the fundamentals of machine learning, including supervised, unsupervised, semi-supervised, and reinforcement learning, and how deep learning uses neural networks to learn from data.
Compare supervised learning and unsupervised learning using labeled and unlabeled data to reveal when models map inputs to outputs and discover patterns.
Reinforcement learning lets an algorithm learn decision making by interacting with an environment, receiving rewards or penalties, and through trial and error, powering robotics, gaming, AI, and autonomous vehicles.
Deep learning, a subset of machine learning, uses multi-layer neural networks to learn hierarchical features from raw data and powers end-to-end applications in computer vision, natural language processing, and speech.
Leverage foundation models as pre-trained, large-scale AI frameworks loaded with data to be fine-tuned for diverse tasks. Explore their transfer learning capability, multimodal processing, and applications in NLP and vision.
Explore the fundamentals of generative AI and how foundation models learn from unstructured data to create text, images, and music. Compare discriminative and generative models and their multitask potential.
Explore large language models as general-purpose language machines trained on vast text data. Learn how data, architecture, and training shape their capabilities, and compare open source and closed source LMS.
Explore hallucinations in large language models, understand why they occur, see examples of misattribution and false facts, and learn prompts and active mitigation techniques to minimize inaccuracies.
Discover how GPUs accelerate generative AI with parallel matrix and vector processing, speeding up training and large-scale image and video tasks.
Explore a five-stage workflow for building specialized AI models from a foundation model, including data preparation, tokenization, training, validation, prompts, and deployment.
Explore transformers, the encoder-decoder architecture with self-attention that enables parallel processing and understanding of entire sequences, powering language translation, text generation, and other generative AI tasks.
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