
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.
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 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.
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