CUDA Crash Course
Comprehensive introduction to CUDA programming, covering vector operations, matrix multiplication, memory optimization, and performance tuning for GPU-accelerated computing.
Z-Transform
Comprehensive exploration of Z-Transform concepts, including inverse methods, properties, and practical applications, with additional insights on CSIR NET exam preparation.
Image Processing in MATLAB
Learn essential image processing techniques in MATLAB, from basic operations to advanced filters and edge detection. Gain practical skills for manipulating, analyzing, and enhancing digital images efficiently.
Graph Neural Networks- A Gentle Introduction
Gentle introduction to Graph Neural Networks, covering key concepts, properties, and variants. Learn about graph representation, information propagation, and common tasks in this emerging field of machine learning.
Digital Signal Processing - MATLAB Helper
Explore Digital Signal Processing with MATLAB: from impulse responses to Fourier transforms, filter designs, and spectral analysis. Gain practical skills for real-world DSP applications.
Signals and Systems
This course is a study of signals and systems, covering topics: formal definition of 'signal' and 'system', continuous and discrete signals, continuous and discrete-time systems, Linear Time-Invariant (LTI) systems, representation of continuous and discr…
Digital Signal Processing and Its Applications
Explore digital signal processing fundamentals, including sampling, Fourier transforms, discrete systems, filters, and various transform techniques. Gain practical skills in filter design and implementation.
Filtering in Computer Vision - Lecture 2
Learn about image filtering techniques, including noise reduction, edge detection, and smoothing. Explore applications in computer vision and gain hands-on experience with MATLAB functions for image processing.
CAP5415 - Digital Image Processing: Filtering and Noise Reduction - Lecture 3
Explore image processing fundamentals, including digitization, histograms, noise types, and filtering techniques. Learn about derivatives, averages, and correlation in the context of digital image analysis.
Subspace Capsule Network
Explore Subspace Capsule Networks: a novel approach to neural networks that preserves information, norms, and angles. Learn about its principles, applications, and advantages in image classification and generation.
Image Filtering, Convolution, and Edge Detection
Explore image processing techniques including filtering, convolution, and edge detection. Learn about derivatives, noise reduction, and various edge detection algorithms for computer vision applications.
Introduction to Convolutional Neural Networks - Part 1 - Lecture 6
Comprehensive introduction to Convolutional Neural Networks, covering history, architecture, key concepts, and applications in image processing and beyond.
Filtering in Computer Vision - Lecture 2
Learn about image filtering techniques, including noise reduction, derivatives, convolution, and Gaussian filters. Explore practical applications in computer vision using MATLAB.
Digital Signal Processing Lectures, Fall 2014
Comprehensive lectures on digital signal processing, covering signals, systems, transforms, filtering, and advanced topics. Ideal for engineering students seeking in-depth understanding of DSP concepts and applications.
DNA Language Models and Convolution - Lecture 12
Dive into DNA language models and convolution techniques, exploring advanced computational approaches for analyzing genetic sequences and structures.
Partial Differential Equations
Explore advanced mathematical concepts including Laplace equations, convolution, polar coordinates, and more. Master techniques for solving complex differential equations and their applications.
Computer Vision Tutorial - Image Processing - Convolution Neural Network
This Computer Vision Tutorial will introduce you Computer Vision and take you deep into concepts and practical implementation of the subject.
Convolution
Explore convolution through intuitive descriptions, equations, and worked examples of typical functions, covering various applications and related concepts.
Laplace Transforms and Solving ODEs
Explore Laplace Transforms to solve complex differential equations, focusing on discontinuities and impulses. Learn key properties, applications, and techniques for various mathematical scenarios.
Random Walks II - Lecture 13
Explore random walks, Pascal's triangle, and probability distributions using Julia programming. Learn to implement time evolution of probabilities and understand cumulative sums in computational thinking.
Transforming Images and Image Processing Techniques - Lecture 2
Explore image transformation techniques using Julia, including downsampling, linear combinations, convolutions, and filters. Learn about complexity, GPUs, and data structures in computational thinking.