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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.
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 Convolutional Neural Networks for Computer Vision with MIT's Alexander Amini. Learn feature extraction, non-linearity, and applications in under an hour.
Dive into deep learning with MIT's concise material on Recurrent Neural Networks, covering sequence modeling, LSTM, and RNN applications in under an hour.
Dive into deep learning with MIT's concise program. Understand perceptrons, neural networks, activation functions, and more in under an hour.
Dive into machine learning with MIT's short program, featuring a Google Brain guest lecture. Explore data visualization, high dimensionality, and language systems.
Dive into Deep Learning with MIT's less than 1-hour material on Image Domain Transfer, featuring a guest lecture from NVIDIA. Explore neural networks, artistic style transfer, and more.
Dive into deep learning with MIT's less than 1-hour material, exploring biologically plausible learning algorithms for neural networks, offered by Alexander Amini.
Explore deep learning limitations and new frontiers with MIT's Alexander Amini. Understand neural networks, adversarial attacks, uncertainty, and AutoML in under an hour.
Dive into Deep Reinforcement Learning with MIT's Alexander Amini. Understand key concepts, Q-function, algorithms, and policy gradient in under an hour.
Dive into Deep Generative Modeling with MIT's Alexander Amini. Learn about autoencoders, VAEs, GANs, and more in under an hour.
Dive into deep learning with MIT's concise material on Convolutional Neural Networks, covering feature extraction, backpropagation, and real-world applications.
Dive into deep sequence modeling with recurrent neural networks in this MIT lecture. Learn to predict words, understand long-term dependencies, and explore LSTM and RNNs.
Dive into the foundations of Deep Learning with MIT's Alexander Amini. Learn about perceptrons, neural networks, loss optimization, and more in under an hour.
Explore end-to-end learning strategies for autonomous driving with Alexander Amini's talk. Learn about steering bounds, decision-making challenges, and integrating uncertainty estimation. Less than 1-hour workload.
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