Programming Project: Image Recognition

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Related lectures (26)

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Explores Transformers in computer vision, focusing on 'Attention Is All You Need' architecture and its applications in visual tasks.

MaxPooling as inductive bias for images

Explores how MaxPooling enforces an inductive bias towards local translation invariance in convolutional neural networks.

Deep Learning Fundamentals

Introduces the fundamentals of deep learning, covering neural networks, CNNs, special layers, weight initialization, data preprocessing, and regularization.

Visual Intelligence: Machines and Minds

Explores the history and techniques of computer vision, covering image formation, transformation, dynamic perspectives, and 3D estimation cues.

Computer Vision History Recap

Offers a historical overview of computer vision, exploring key developments and influential figures in the field.

Modern Convolutional Networks and Image Recognition

Explores the evolution of deep convolutional networks and their impact on image recognition accuracy.

Topology: Polyhedral Products and Edge Detection

Explores topology concepts and edge detection in computer vision, highlighting the significance of contours and gradients in image analysis.

Visual Intelligence: Machines and Minds

Explores visual intelligence, image formation, computer vision, and representation understanding in machines and minds.

Visual Intelligence: Machines and Minds

Explores visual intelligence, covering image formation, perception, computer vision, correspondence learning, motion analysis, and recognition in videos.

Transformer Architecture: The X Gomega

Delves into the Transformer architecture, self-attention, and training strategies for machine translation and image recognition.