Computer Vision: Historical Insights and Project Inspirations

Related lectures (30)

Page 2 of 3

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

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

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

Explores the evolution of edge detection techniques, from Canny to deep learning insights.

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

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

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Covers visual recognition models, scene graphs, neural networks, and embodied vision.