Neural networks and fuzzy systems

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

Le robot Figure AI choque le monde ! Les robots progressent plus vite que prévu ! Helix02

Figure AI robot shocks the world! Robots are progressing faster than expected! Helix02

Genie 3, l'IA devient vraiment intelligente en jouant ! Une leçon !

Genie 3, AI becomes really smart by playing! A lesson!

PAINT: Parallel-in-time Neural Twins || Jan 30, 2026

Lec 9: Introduction to Deep Learning - I

AlphaGenome author roundtable

Lec 8: Neural Network – III(Training of the Multi Layer Perceptron)

Lec 7: Neural Network - II (Multi Layer Perceptron)

Lec 6: Neural Network - I (Single Layer Perceptron)

Jürgen Schmidhuber on Robotics, World Models, and NNAISENSE (Oral History Pt. 2)

DeepSeekV4 - Manifold Constrained Hyper Connections (mHC) and the evolution of ResNets

Conditional Gaussian Koopman Network || PINNs with Fourier-enhanced Features || Jan 9, 2026

7: Deep Learning for Natural Language – Transformers

4: Deep Learning for Computer Vision – Transfer Learning and Fine-Tuning; Intro to HuggingFace

3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch

2: Training Deep NNs (cont.); Introduction to Keras/Tensorflow; Application to Tabular Data

1: Introduction to Neural Networks and Deep Learning; Training Deep NNs

On s’est trompés sur les RÉSEAUX de NEURONES (voilà pourquoi)

We Got NEURAL NETWORKS All Wrong (Here’s Why)

CNN Explained Visually: Padding, Stride, Pooling, Receptive Fields, Dilation & Layer Architecture

Transformer-Inspired Physics-Informed DeepONet|| From RoPINN to ProPINN ||Dec 19, 2025

PhD Thesis Defense - Jonathan Courtois – Université Côte d’Azur · CNRS · LEAT : Sparsity in SNNs

MLP Live session

Hands-on Machine Learning -- Training Deep Neural Networks

[ИАД, осень 2025] Методы глубокого обучения. Занятие 13: Acceleration, KV-Cache, Flash Attention

[IAD, fall 2025] Deep learning methods. Class 13: Acceleration, KV-Cache, Flash Attention

Stanford CS230 | Autumn 2025 | Lecture 10: What’s Going On Inside My Model?

Titans: Learning to Memorize at Test Time (Paper Analysis)