Courses from 1000+ universities
17 years ago, Krishna Kumar started offering free PMP prep online. Today, it’s a leading digital upskilling platform that helps millions upskill in AI, cybersecurity, data science, and more.
600 Free Google Certifications
Fundamentals of Neuroscience, Part 1: The Electrical Properties of the Neuron
Organic Chemistry 1
Mountains 101
Organize and share your learning with Class Central Lists.
View our Lists Showcase
Explore how network connectivity shapes neural activity using combinatorial threshold-linear networks. Learn about nerve theorems and their application in understanding fixed points of large neural networks.
Explore topological complexity using arbitrary covers, focusing on sectional category, ANRs, and generalized concepts. Gain insights into advanced algebraic topology techniques and applications.
Explore algebraic geometry techniques to detect robustness in reaction networks, focusing on absolute concentration robustness and its implications for biological systems.
Explore topological data analysis in deep learning, reducing data needs and enhancing transparency. Focuses on image and video analysis, network architectures, and applications in various fields.
Explore data complexes, topological obstructions in database JOIN operations, and persistence levels in data alignment. Learn about measure theory, homotopy, and homology in the context of data integration.
Explore cellular sheaves, Laplacians, and their applications in social networks and opinion dynamics. Learn about decentralized methods for computing sheaf cohomology and analyzing consensus and polarization.
Explore the Gromov-Hausdorff distance between spheres, its applications in geometry and topology, and methods for calculating precise values between specific sphere pairs.
Explore topological clustering of multilayer networks, focusing on higher-order interactions and local neighborhood shapes. Learn applications in climate-insurance and COVID-19 data analysis.
Explore robotic football strategies, discussing modeling, non-fibrations, speed, sensing, and topological complexity in the context of navigating blockers to score.
Explore Ollivier Ricci curvature in complex networks, its applications in network analysis, and implementation in chemical reaction networks using optimal transport theory.
Graph representation learning techniques for biomedical applications, focusing on SubGNN for disentangled subgraph embeddings and their use in predicting disease treatments and safe drug combinations.
Explore liquid crystal phase transitions through persistent homology, revealing thermodynamic features and structural changes in nanocomposites using advanced data analysis techniques.
Explore topological complexity in graph braid groups, focusing on Farber's conjecture about ordered configuration spaces and its implications for topological robotics.
Explore nonlinear dynamics through combinatorial topology, offering efficient computational methods and new perspectives for multiscale systems and data-driven science in biology and engineering.
Explore locally persistent categories, metric properties of interleaving distances, and their applications in algebraic topology, including natural interleavings and weak equivalences.
Get personalized course recommendations, track subjects and courses with reminders, and more.