Social Media Images: Understanding Food Perception with Deep Learning

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Statistical Learning Theory: Conclusions on Deep Learning

Covers the conclusions on deep learning and an introduction to statistical learning theory.

Non-Conceptual Knowledge Systems: Style Transfer and Image Translation

Explores style transfer, image translation, self-supervised learning, video prediction, and image description generation using deep learning techniques.

Convolutional Neural Networks

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

Transformers: Unifying Machine Learning Communities

Covers the role of Transformers in unifying various machine learning fields.

Computer Vision: Fundamentals and Applications

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

Texture Analysis: Statistical and Structural Approaches

Discusses texture analysis in images, focusing on statistical and structural properties, segmentation techniques, and machine learning applications for texture classification.

Machine Learning for Physicists/Chemists: Image Classification

Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.

Convolutional Neural Networks

Introduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.

Deep Visual Recognition: Interpretability

Explores deep visual recognition, interpretability, CNN architectures, visual dictionaries, and attention mechanisms.

Computer Vision: Image Formation and Transformation

Provides an overview of image formation and transformation principles in computer vision.