UpstatePedro

controllabledl

ConvNeXt

Code release for ConvNeXt model

corpora

Summarise text in a collection of docs by finding the most common words

sentipy

Python implementations of Sentinel 2 Toolbox L2 products & spectral indices

databricks-zero-to-mlops

deep-learning-containers

AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.

DENETHOR

dlaicourse

Notebooks for learning deep learning

Domain-Aware-Style-Transfer

Official Implementation of Domain-Aware Universal Style Transfer

DUE

Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".

fastbook

The fastai book, published as Jupyter Notebooks

field-delineation

Field delineation with Sentinel-2 data from Sentinel-Hub and a ResUnet-a architecture.

idea-bank

thought gutter for a wandering mind

minitorch

The full torch API in python (from https://github.com/minitorch/minitorch)

mlops-on-gcp

nbdev-poc

Taking NBDev for a joyride

nde_playground

Exploration of Neural ODE, CDE, SDE architectures.

NeuralCDE

Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)

NeuralCDEcrops

neuralRDEs

Code for: "Neural Rough Differential Equations for Long Time Series", (ICML 2021)

presto

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

PyAEZ

PyAEZ is a python package consisted of many algorithms related to Agro-ecalogical zoning (AEZ) framework.

PySparkPlayground

Taking the PySpark programming model for a walk...

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

s1-s2-ard

Creates analysis read data (ARD) from sentinel data. Creates multi-modal and/or multi-temporal Sentinel-1 and Sentinel-2 ARD.

satellite-cloud-removal-dip

Satellite cloud removal with Deep Image Prior.

tpu

Reference models and tools for Cloud TPUs.

UMNN

Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for modelling monotonic transformations in normalizing flows.

uncertainty-baselines

High-quality implementations of standard and SOTA methods on a variety of tasks.