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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.
Explore deep learning and its applications in computer vision and social networks with this MIT program. Learn about image enhancement, video classification, and more in under an hour.
Dive into deep learning with MIT's concise program on sequence modeling with neural networks, featuring guest lectures from Google, NVIDIA, IBM, and Tencent.
Explore deep learning and reasoning with MIT's Alexander Amini in under an hour. The program includes lectures from IBM, Google, NVIDIA, and Tencent guests.
Explore deep learning's limitations and new frontiers in this concise, less than 1-hour program by Alexander Amini, featuring guest lectures from Google, NVIDIA, IBM, and Tencent.
Dive into Deep Learning with MIT's concise program. Learn about computer vision, feature extraction, convolutional layers, and real-world applications in under an hour.
Dive into Deep Learning with Alexander Amini's MIT lectures, covering foundations, sequence modeling, computer vision, generative models, and more in under an hour.
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 the evolution of AI, understand the concept of Neurosymbolic AI, and delve into the advantages of combining symbolic AI in this less than 1-hour material by Alexander Amini.
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