Weinstein Manifolds Through Skeletal Topology - Laura Starkston
Explore skeletal topology's role in understanding Weinstein manifolds, focusing on arboreal singularities, Fukaya categories, and stratification of skeletons in symplectic geometry.
Machines - How Do They Learn and Where Are They Headed?
Explore machine learning fundamentals, neural networks, and AI challenges. Gain insights into how machines learn, their capabilities, and future directions in artificial intelligence.
New Physics at the LHC and Beyond I - Liantao Wang
Explore cutting-edge particle physics research at the LHC, including luminosity, hadron colliders, and factorization. Gain insights into new physics prospects and theoretical advancements in the field.
The Polynomial Method and the Restriction Problem - Larry Guth
Explore the polynomial method and restriction problem in mathematics, featuring insights on visualizing concepts, analyzing proofs, and examining algebraic potential.
Math in the Real World - More Than Just a Numbers Game
Explore how mathematics shapes product lifecycles and impacts modern careers, with insights from industry leaders on its role in R&D, manufacturing, marketing, and supply chain.
Metasomatism and Differentiation - Petrology
Explore metasomatism types, principles, and differentiation in igneous petrology. Gain insights into chemical changes in rocks and their geological implications.
Linear Regression Using Sci-Kit Learn in Python
Learn to implement linear regression using Sci-Kit Learn in Python, from data import to model creation, evaluation, and prediction. Hands-on coding with real-world data.
Scratch Assay Analysis With Just 5 Lines Code in Python
Learn to analyze scratch assays efficiently using Python, covering entropy filtering, Otsu thresholding, image processing, and result plotting in just 5 lines of code.
Deep Generative Modeling
Explore deep generative modeling, including autoencoders, variational autoencoders, and GANs. Learn about latent variable models, reparameterization tricks, and recent advances in generative AI.
Deep Generative Modeling
Explore deep generative modeling, including autoencoders, VAEs, and GANs. Learn about latent variables, representation learning, and applications in image generation and domain transformation.
Create a Leprechaun - Pavlovich Workshop - Michael Pavlovich - ZBrush 2021
Learn to create a detailed Leprechaun character in ZBrush, covering techniques like base mesh manipulation, dynamesh blockout, topology refinement, and fibermesh hair creation for a professional 3D model.
Modeling Basics - Booleans - Pixologic Joseph Drust - ZBrush 2020
Explore ZBrush 2020's modeling basics, focusing on Boolean operations, ZModeler, and IMM Boolean brushes. Learn subtractive techniques, navigation, and efficient workflows for 3D sculpting and mesh creation.
ZBrush 2020 - Modeling Basics: Tire with ZModeler - Pixologic Joseph Drust
Learn ZBrush 2020 modeling basics, focusing on tire creation with ZModeler. Master essential tools and techniques like brush selection, ZModeler actions, and dynamic subdivision for efficient 3D sculpting.
Unreal Engine Modeling Tools Tutorial - Creating a Greek Column
Learn to create a Greek column in Unreal Engine using modeling tools. Master techniques for variation, polygroups, and booleans to enhance your game development skills.
How to Get Started with Data Science and Machine Learning
Explore the fundamentals of data science, analytics, AI, and machine learning. Learn essential skills and create a structured learning plan for these in-demand fields.
Understand Image Size in Photoshop - Magic AI Resizing
Learn image resizing techniques in Photoshop, including AI-powered options, for various purposes like print and screen. Understand interpolation, document shape changes, and file size compression.
Understand Resolution in Photoshop
Learn to grasp image size and resolution in Photoshop, including DPI, PPI, screen resolution, and resampling. Gain insights on viewing images and understanding size differences.
Using Discrepancy Theory to Improve the Design of Randomized Controlled Trials - Daniel Spielman
Explore discrepancy theory's application in randomized controlled trials, enhancing experimental design and statistical analysis for more accurate and efficient research outcomes.
Learning Probability Distributions - What Can, What Can't Be Done - Shai Ben-David
Explore probability distribution learning, its possibilities and limitations. Shai Ben-David discusses density estimation, sample compression schemes, and challenges in statistical learning theory.
Latent Stochastic Differential Equations for Irregularly-Sampled Time Series - David Duvenaud
Explore advanced techniques for analyzing irregularly-sampled time series data using latent stochastic differential equations, with applications in predictive modeling and continuous-time machine learning.
Ellipses of Small Eccentricity Are Determined by Their Dirichlet - Steven Morris Zelditch
Explore the spectral analysis of ellipses with small eccentricity, focusing on Dirichlet and Neumann spectra, caustic billiard tables, and periodic orbits in nearly circular domains.