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Deep learning, LLMs, and AI applications in finance and technology.
Mathematics, programming, and core concepts for AI/ML
Supervised, unsupervised, and traditional ML algorithms
Neural networks, CNNs, RNNs, and transformers
LLMs, diffusion models, and multimodal AI
NLP, computer vision, RL, and graph neural networks
ML system design, production ML, and responsible AI
Foundation models, AI for science, and robotics
A deep dive into how Large Language Models work, from transformers to GPT architecture.
