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Explore convolutional neural networks for computer vision, covering feature extraction, convolution operations, and applications like object detection and self-driving cars.
Comprehensive lecture on Recurrent Neural Networks, covering theory, implementation, and applications. Explores sequence modeling, LSTM, attention mechanisms, and practical examples in deep learning.
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.
Explore machine learning applications in digitizing scent, from understanding olfactory processes to predicting odor descriptors using graph neural networks and interpreting molecular fragrance data.
Explore neural rendering techniques, from forward and inverse rendering to 3D data representations, with insights on RenderNet, neural point-based graphics, and HoloGAN in this comprehensive lecture.
Explore generalizable autonomy in robot manipulation through imitation learning, visuo-motor policies, and reinforcement learning techniques. Discover innovative approaches for achieving adaptable and efficient robotic systems.
Explore neurosymbolic AI, combining deep learning and symbolic reasoning to address limitations in current AI systems. Learn about out-of-distribution performance, adversarial examples, and advantages of hybrid approaches.
Explore deep learning limitations and new frontiers, including expressivity, generalization, adversarial attacks, uncertainty, and AutoML. Gain insights into cutting-edge developments in neural networks.
Explore deep reinforcement learning concepts, from Q-functions to policy gradients, with applications in Atari games, real-world scenarios, and groundbreaking AI like AlphaGo.
Explore deep generative modeling, including autoencoders, variational autoencoders, and GANs. Learn about latent variable models, reparameterization tricks, and recent advances in generative AI.
Explore convolutional neural networks for computer vision, covering feature extraction, network architecture, and real-world applications like self-driving cars in this comprehensive lecture.
Explore recurrent neural networks, LSTMs, and attention mechanisms for sequence modeling. Learn backpropagation through time, gradient issues, and practical applications in deep learning.
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.
Explore data visualization techniques for machine learning, including high-dimensional data, multilingual systems, and language models, with insights on biases and user intent.
Explore image domain transfer techniques, from artistic style transfer to multimodal translations, with applications in photo smoothing, seasonal transformations, and game engines.
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