Computer Vision: History Recap & Logistics

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 3 of 4

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Deep Learning: Exploring Vision and Language Transformers

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

Rule Systems, Simulations, and Parallel Worlds

Delves into rule systems, simulations, and parallel worlds, exploring Prolog, backtracking algorithms, logic complexity, the Game of Life simulation, and the concept of Simulats.

Deep Learning: Edge Detection and Neural Networks

Discusses edge detection techniques and the evolution of deep learning in neural networks.

Propositional Logic: Inference Rules

Covers the interpretation of propositional logic and inference rules for implication, conjunction, and double negation.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Edge Detection: Deep Learning Insights

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

Quantum Machine Learning: Theory and Applications

Explores quantum machine learning, representations of molecules, kernel regression, and the interplay between physics and machine learning.

Nonlinear Supervised Learning

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

PyTorch and Convolutional Networks

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