Social Image Systems: From Past to Present

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Geometric Insights on Deep Learning Models

Delves into the geometric insights of deep learning models, exploring their vulnerability to perturbations and the importance of robustness and interpretability.

Fully Connected Networks on MNIST and SUSY Datasets

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

Computer Vision: Fundamentals and Applications

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

Generative Adversarial Networks: Data Synthesis Techniques

Discusses Generative Adversarial Networks and their applications in synthesizing data and generating images.

Convolutional Neural Networks: Structure & Regularization

Explores Convolutional Neural Networks, data augmentation, weight decay, and dropout for enhancing model performance.

Evaluating Machine Accuracy and Robustness on ImageNet

Explores the evaluation of machine and human accuracy and robustness on ImageNet, highlighting progress, challenges, and the need for improvement.

Machine Learning for Feature Extraction

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Uncertainty Quantification and Label Error Detection

Explores uncertainty quantification and label error detection in deep learning for semantic segmentation, focusing on challenges and methods for error detection.

Computer Vision Basics: Image Processing and Feature Detection

Covers the basics of computer vision, focusing on image processing techniques and feature detection.

Machine Learning Fundamentals

Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.