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Dive into deep generative modeling with this MIT lecture series. Learn about autoencoders, variational autoencoders, generative adversarial networks, and more in just 1-2 hours.
Dive into Convolutional Neural Networks for Computer Vision with MIT's Alexander Amini. Learn feature extraction, object detection, and more in under an hour.
Explore deep learning and speech recognition with MIT's Alexander Amini. Learn from Rev.com's AI R&D Head and Senior Speech Scientist in under an hour.
Dive into Deep Reinforcement Learning with Alexander Amini's MIT lecture. Understand key concepts, algorithms, and real-life applications in under an hour.
Dive into deep generative modeling with MIT's Alexander Amini. Learn about supervised and unsupervised learning, autoencoders, and GANs in under an hour.
Explore Convolutional Neural Networks for computer vision with MIT's Alexander Amini. Learn about feature extraction, spatial structure, and applications in healthcare and autonomous navigation.
Dive into deep learning with MIT's concise material on recurrent neural networks and transformers, covering sequence modeling, LSTM, and attention fundamentals.
Dive into deep learning with MIT's Alexander Amini. Understand perceptrons, neural networks, activation functions, and more in under an hour.
Explore AI applications in healthcare, including lung cancer screening, pathology, genomics, and more in this less than 1-hour material by Alexander Amini.
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.
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