Convolutions in Image Processing - MIT 18.S191 Fall 2020 - Week 1
Explore image processing convolutions, from basic blurs to edge detection, using Julia. Learn about kernels, computational complexity, and connections to Fourier transforms.
Linear Systems of Differential Equations with Forcing - Convolution and the Dirac Delta Function
Explores linear differential equations with forcing, covering impulse response, Dirac delta function, and convolution integral. Essential for control theory and linear systems analysis.
An Overview of Deep Learning and Neural Networks
Comprehensive introduction to deep learning concepts, covering neural networks, convolution, pooling, normalization, and more. Includes code examples for practical implementation.
How Convolutional Neural Networks Work, in Depth
Comprehensive exploration of convolutional neural networks, covering their structure, operation, and training process, including key concepts like filtering, pooling, and backpropagation.
Processamento de Imagens - Aula 05
Aprofunde-se no processamento de imagens com o Professor Luiz Velho, explorando técnicas avançadas e aplicações práticas nesta aula de mestrado.
Processamento de Imagens - Aula 04
Explore técnicas avançadas de processamento de imagens em nível de mestrado, abordando conceitos e métodos essenciais para manipulação e análise de dados visuais.
Advances in Wave Imaging and Microlocal Analysis - SPACE Webinar Series
Explore wave imaging techniques, from traditional approaches to new methods, with a focus on microlocal analysis, Fourier integral operators, and neural network architectures for improved imaging.
SPACE Webinar: Imaging Techniques for Museum Painting Analysis
Explore advanced imaging techniques for analyzing painting layers, including fluorescence, X-ray, and machine learning methods for art conservation and authentication.
Hans Feichtinger- Fourier Analysis via the Banach Gelfand Triple
Explore Gabor analysis foundations using MATLAB, focusing on finite Abelian groups and matrix analysis methods. Learn to apply abstract harmonic analysis concepts to signal processing problems.
Wavelets- A Mathematical Microscope
Explore wavelet transform for signal processing, uncovering hidden structures in data. Learn to build a wavelet toolkit, understanding its applications from hydrodynamics to neuroscience.
Using Convolution for Archeo-Acoustic Conservation
Explore computational techniques for preserving unique acoustic properties of historical spaces, enhancing heritage conservation through audio technology.
Hilbert Transforms - Introduction and Fundamentals - Part 1
Explore Hilbert Transforms fundamentals in signal processing. Learn key concepts, applications, and mathematical foundations for analyzing complex signals and systems.
Convolution and the Fourier Series
Explore convolution and its relation to Fourier series, unraveling complex mathematical concepts through clear explanations and real-world analogies.
Sobolev Regularity for Maximal Operators
Explore recent developments in regularity theory for maximal operators, focusing on Sobolev and BV data. Accessible overview of an active research area with open questions.
Matrix Multiplication, Signals, and Convolutions
Explore matrix multiplication, natural signals, and convolutions in deep learning. Includes practical demonstrations using IPython and PyTorch.
The Curious World of Integral Polynumbers - Math Foundations 233
Explore integral polynumbers through particle physics concepts, expanding arithmetic to include integers as coefficients and exponents, and viewing polynomials as algebraic objects rather than functions.
Classical and Free Zero Bias for Infinite Divisibility
Explore classical and free zero bias in infinite divisibility with Larry Goldstein, delving into probability theory and statistical concepts.
Resurgent Perverse Sheaves and Topological Resurgence Theory
Explore topological counterparts of resurgence theory using perverse sheaves on C as convolution algebras, extending beyond regular holonomic D-modules to infinitely singular cases.
Deep Learning: Convolutional Neural Networks and CNN Architecture - Lecture 6
Dive into the fundamentals of Convolutional Neural Networks (CNNs), exploring convolution operations, cross-correlation techniques, and pooling layers for advanced deep learning applications.
Neural Nets for NLP 2017 - Convolutional Networks for Text
Comprehensive lecture on convolutional networks for text, covering bag-of-words, n-grams, applications, stacked and dilated convolutions, structured convolution, sentence pair modeling, and CNN visualization techniques.
Graph Neural Networks Implementation in Python
Learn Graph Neural Networks implementation in Python, covering graph representations, node embeddings, message passing, and practical applications using NetworkX and PyG libraries.