Процесс ревью на ICLR

DALL-E in Pytorch (wip)
Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch: https://github.com/lucidrains/DALLE-pytorch: https://github.com/lucidrains/DALLE-pytorch
Those who do not want to imitate anything, produce nothing.…

Soft-DTW: a Differentiable Loss Function for Time-Series
Marco Cuturi, Mathieu Blondel: https://arxiv.org/abs/1703.01541
#ArtificialIntelligence #DeepLearning #TimeSeries

SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Batzner et al.: https://arxiv.org/abs/2101.03164
#ComputationalPhysics #MaterialsScience #MachineLearning

Обсуждение библиотек машинного обучения Rapids (вычисления на видеокартах) на платформе mset.space

#020 ML Английский язык для машинного обучения

OpenAI DALL·E: Fighter Jet For The Mind! ✈️

Lecture 14 - Data Loading - CIFAR10 Deep Learning Computer Vision Pytorch Full Course

My Machine Learning Model Is Perfect

Using Deep Reinforcement Learning to create a Chess Algorithm:

Создаем нейронную сеть на Keras с нуля ➤ Искусственный интеллект на Python.

10 Imperial's Deep learning course: Batch Norm

08 Imperial's Deep learning course: VGG

CW0 Imperial's Deep learning course: Introduction to PyTorch The Basics

A walkthrough of my Machine Learning approach in a data Science competition

SVM Classifier and RBF Kernel — How to Make Better Models in Python

Convolutional Neural Network Visualization I keras

Python NumPy Tutorial for Beginners #3 - Slicing Arrays

oLMpics — On what Language Model Pre-training Captures
Talmor et al.: https://arxiv.org/abs/1912.13283
#ArtificialIntelligence #DeepLearning #MachineLearning

Technology Readiness Levels for Machine Learning Systems
Lavin et al.: https://arxiv.org/abs/2101.03989
#DeepLearning #TechnologyReadinessLevels #MachineLearning