Computer Vision: Fundamentals and Applications

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Related lectures (30)

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Deep Learning: Convolutional Neural Networks and Training Techniques

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

Convolutional Neural Networks

Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.

Optical Computing: All-Analog Photoelectronic Chip for Vision Tasks

Presents an all-analog photoelectronic chip for high-speed vision tasks, addressing challenges in classical computation and proposing a hybrid optical-electrical framework.

Convolutional Neural Networks

Introduces Convolutional Neural Networks, covering fully connected layers, convolutions, pooling, PyTorch translations, and applications like hand pose estimation and tubularity estimation.

PyTorch and Convolutional Networks

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

Deep Learning: Convolutional Neural Networks

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Neural Networks for NLP

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

Fully Connected Networks on MNIST and SUSY Datasets

Covers the implementation of fully connected neural networks on two datasets using PyTorch.

Deep Learning: Exploring Vision and Language Transformers

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