Neural networks and fuzzy systems

MLP Live session | Week 3 Session 2

[Generative AI in Urdu/Hindi] Lecture 15: Methods of computing attention

Beyond the Buzzwords: How Neural Networks Actually Learn

[ИАД, весна 2026] Введение в машинное обучение. Лекция 3: Нейронные сети

[IAD, Spring 2026] Introduction to Machine Learning. Lecture 3: Neural Networks

Renier Mendoza: Image reconstruction in electrical impedance tomography using deep neural networks

Pantelis Vlachas: Hypernetworks for Adaptive and Generalizable Forecasting in Dynamical Systems

Masanobu Horie: Structure-Preserving Graph Neural Networks: Enforcing Symmetry and Conservation Laws

Luca Galimberti: Infinite-dimensional Deep Learning methods for Finance

Krishnan Raghavan: The role of architecture in learning a continuum of task

Florian Rossmannek: Echo states and fading memory in state-space systems

Deepseek Sparse Attention

Wei Kang: Deep Learning for Data Assimilation

MLP Live session | Week 3

Benjamin Charles Walker: Linear Neural Controlled Differential Equations

Masanobu Inubushi: Reservoir computing with generalized readout based on generalized synchronization

Grok 4.2 est intelligent … mais genre SUPER INTELLIGENT !

Grok 4.2 is smart … but like SUPER SMART!

MLP Live session | Week 2

Graph Sample and Aggregate: GraphSAGE

Graph Neural Networks: what are they?

Graph Neural Networks: message passing

Graph Convolutional Networks

Graph Attention Networks (GAT)

[Generative AI in Urdu/Hindi] Lecture 13: LSTMs, bi-directional, multi-layer, teacher forcing.

MLP Live session | Week 2

[Generative AI in Urdu/Hindi] Lecture 12: RNN variations, its issues, possible solutions. LSTMs.

Randomized Greedy Algorithms for Neural Network Optimization || Feb 13, 2026

MLP Project Orientation | 26T1

[Generative AI in Urdu/Hindi] Lecture 11: RNNs training, derivations, applications and more!

PyTorch Tutorial

Lec 09. Hacker's Guide to Deep Learning

Lec 08. Architectures: Transformers

Lec 05. Architectures: Graphs

Lec 04. Architectures: Grids

Lec 03. Approximation Theory

Lec 02. How to Train a Neural Net

Lec 01. Introduction to Deep Learning

[Generative AI in Urdu/Hindi] Lecture 10: Feed-forward NNs, pooling layer, sequence modelling, RNNs

Manifold Constrained HyperConnections

Lec 14: Convolutional Neural Network - I

[Generative AI in Urdu/Hindi] Lecture 9: Developing a complete feed-forward neural network