Neural networks and fuzzy systems

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)

Geoffrey E. Hinton: Deep learning with multiplicative interactions

Yann LeCun: Learning Hierarchies of Features

Tara Sainath: The Latest in DNN Research at IBM: DNN-based features, Low-Rank Matrices for Hybrid...

Francesco Martinuzzi: Learning Single and Multiple Chaotic Systems with Minimal Reservoir Computing

[ИАД, осень 2025] Методы глубокого обучения. Лекция 12: Multimodality, CLIP, BLIP, LLaVA

[IAD, fall 2025] Deep Learning Methods. Lecture 12: Multimodality, CLIP, BLIP, LLaVA

Live from NeurIPS: Meet the Researchers | Nemotron Labs

Become an AI Researcher Course – LLM, Math, PyTorch, Neural Networks, Transformers

Google quiere cambiar la IA para siempre (Nested Learning)

Google wants to change AI forever (Nested Learning)

Mohammad Tabish: Data-driven operator learning: Dictionary optimization and randomized neural nets

Deep Learning for intrusion detection in emerging technologies with Dr. Euclides Neto

He Co-Invented the Transformer. Now: Continuous Thought Machines [Llion Jones / Luke Darlow]

Why you should see the world like a large language model | Dan Shipper: Full Interview

Recursive Reasoning with Tiny Networks

Dana Angluin: Trying to Understand Transformers

Understanding PINN’s Failure Modes || Progressive Sharpening from Jacobian Alignment|| Nov 14, 2025