Modern Convolutional Networks and Image Recognition

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Related lectures (32)

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Deep Learning: No Free Lunch Theorem and Inductive Bias

Covers the No Free Lunch Theorem and the role of inductive bias in deep learning and reinforcement learning.

Deep Learning: Convolutional Neural Networks and Training Techniques

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Deep Learning for Autonomous Vehicles: Learning

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

Graph Search: Neural Networks and Deep Learning

Delves into graph search, neural networks, and deep learning, covering topics like convolutional neural networks and artificial neural networks.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

NFNets: Removing BatchNorm for High-Performance Image Recognition

Explores NFNets as an alternative to BatchNorm in ResNets, achieving high performance on ImageNet.

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Hand Pose Estimation

Covers hand pose estimation, regression techniques, and the evolution of image classification models from LeNet to VGG19.

Deep Visual Recognition: Interpretability

Explores deep visual recognition, interpretability, CNN architectures, visual dictionaries, and attention mechanisms.