Transformers in Vision

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Related lectures (29)

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Deep Learning: Edge Detection and Neural Networks

Discusses edge detection techniques and the evolution of deep learning in neural networks.

Visual Prostheses: History and Intracortical Stimulation

Explores the history and challenges of visual prostheses, focusing on intracortical microstimulation for artificial vision.

Vision Research to Clinical Therapy

Explores vision research, optic nerve regeneration, and bio-realistic models in the mouse visual cortex, focusing on optic nerve stimulation and visual learning.

Perceptual Robotics: Integrating Vision and Action

Covers the integration of visual perception and robotic actions in embodied AI.

Neural Taskonomy and Historical Perspectives in Visual Intelligence

Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.

PyTorch and Convolutional Networks

Covers PyTorch tensor data structure and training a CNN to classify images.

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: Convolutional Neural Networks

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

Computer Vision: Fundamentals and Applications

Covers the fundamentals of computer vision, illusions, challenges, applications, and history.

Improving Models of the Ventral Visual Pathway

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.