Concept

Kernel (operating system)

Related lectures (32)

Explores the Mercer Theorem, Kernels, and their role in machine learning applications.

Covers system calls, kernel space, process management, file descriptors, pipes, and hardware isolation in OS organization.

Hensel's Lemma Application

Discusses the application of Hensel's lemma in determining nth roots and elements that are nth powers.

Interrupts and System Calls

Explores interrupts, exception handling, and system calls in operating systems, focusing on CPU behavior and interrupt handling mechanisms.

Kernel Regression: Basics and Applications

Explores kernel regression, the curse of dimensionality, and random features in neural networks.

Feature Expansion: Kernels and KNN

Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.

Jupyter Notebook: Interactive Computing and Computational Narratives

Introduces Jupyter Notebook for composing interactive programs with live code and narrative text, covering installation, code cells, Markdown text, widgets, and kernel management.

Optimal Linear Response: Stochastic Dynamical Systems

Explores optimal linear response for stochastic dynamical systems, addressing perturbations and mixing rate optimization.

Kernel Methods: Machine Learning

Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.

Variety Defined as the Closure of VCA

Explores the concept of variety defined as the closure of VCA and its applications in algebraic geometry.

Advanced Pandas Functions

Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.

The Role of Operating Systems: Foundations and Functions

Covers the essential role of operating systems in managing hardware and enabling applications across various devices.

Kernels: Nonlinear Transformations

Explores kernels for simplifying data representation and making it linearly separable in feature spaces, including popular functions and practical exercises.

Darcy Problem: Discretisations and Coercivity

Covers the Darcy problem, focusing on discretisations and the concept of coercivity.

Exception Handling in Python

Covers exception handling in Python, including try, raise, except, else, and finally blocks, with examples.

Kernel Build Process: Patch Submission

Covers the kernel build process, patch submission, bug reporting, and mailing list archives.

Kernel Regression: K-nearest Neighbors

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Magnetic Tunnel Junctions: Memory Applications

Explores the evolution and challenges of MRAM technology for memory applications and its potential for future computing paradigms.

Interrupts

Explores interrupts as a mechanism for abstraction and multiplexing in operating systems, covering handling, processing, and initialization.

Nonlinear SVM: Kernels and Dual Optimization

Explores transforming data with nonlinear maps, kernels, dual optimization, and interpreting SVM results.