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Explore AI's role in 3D content creation with this MIT lecture. Learn about synthesizing worlds, scene composition, and neural simulation in under an hour.
Learn to tame dataset bias via domain adaptation in this MIT lecture. Understand real-world implications, adversarial domain alignment, and enforcing consistency in under an hour.
Explore deep learning and information extraction with MIT's Alexander Amini. Learn about context-free grammars, parsing, and handling noise in under an hour.
Explore AI bias and fairness in this MIT lecture, covering topics from understanding bias in machine learning to mitigation strategies and future considerations. Less than 1-hour workload.
Dive into deep learning and uncertainty estimation with MIT's Alexander Amini. Learn about probabilistic learning, Bayesian neural networks, and evidential deep learning in under an hour.
Explore deep learning's limitations and new frontiers with MIT's concise material, covering topics like adversarial attacks, algorithmic bias, and AutoML.
Dive into Deep Reinforcement Learning with MIT's Alexander Amini. In under an hour, explore Q functions, policy learning, and real-life applications.
Dive into Convolutional Neural Networks for Computer Vision with MIT's Alexander Amini. Learn feature extraction, object detection, and build self-driving cars in under an hour.
Dive into deep learning with MIT's 1-2 hour material on Recurrent Neural Networks, covering sequence modeling, LSTM, RNN applications, and more.
Dive into deep learning with MIT's concise program. Understand perceptrons, neural networks, activation functions, and more in under an hour.
Explore MIT's deep dive into machine learning for scent, covering topics from digitizing smell to predicting odor descriptors. Less than 1-hour workload.
Dive into deep learning with MIT's concise material on neural rendering, covering topics from forward rendering to HoloGAN. Less than 1-hour workload.
Explore deep learning and robot manipulation with MIT's concise material, covering topics like imitation learning, visuo-motor policies, and neural task programming.
Explore deep learning's limitations and new frontiers in this concise MIT lecture, covering topics from adversarial attacks to AutoML. Less than 1-hour workload.
Dive into Deep Reinforcement Learning with MIT's Alexander Amini. Understand Q functions, Deep Q Networks, policy learning, and real-life applications in under an hour.
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