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resnet_checkpoints

Exploring gradient chekcpoints resnets with fastai V2

rnn_study

rocket

This is a multi-channel implementation of ROCKET

sagemaker-llama2-wandb

smslab_intro

A wandb SageMaker Studio Lab intro notebook

solar_seg

Solar segmentation of PV modules

spacy_streamlit

A simple spaCy component for streamlit

stable-diffusion-tensorflow

Tensorflow port of the Stable Diffusion Model

stam

Space-Time Attention Model

sunset

Stanford University Neural-network for Solar Electricity Trend

sweep_run_cap

Minimal repo for sweep cap

tabular_image_models

A fastai based repo to ensemble image and tabular data.

tcapelle

My banner

tcapelle.github.io

my website

termgpt

A simple wrapper to call openAI's chatGPT on the terminal written in Python

tf-metal-experiments

TensorFlow Metal Backend on Apple Silicon Experiments (just for fun)

tgs

TGS kagle challenge

timeseries_dl_demo

Some example using Deep Learning for timeseries.

timeseries_fastai

fastai V2 implementation of Timeseries classification papers.

timeseries_hack

A simple timeseries baseline with wandb

timesformer

The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"

tinyagent

torch_moving_mnist

A simple Dataset generator for Moving Mnist

torchdata

Exploring new PyTorch 1.11 data API

transformer

Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.

transformer-benchmarks

benchmarking some transformer deployments

tsai

Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

u-2-net

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

vllm

A high-throughput and memory-efficient inference and serving engine for LLMs

wandb-addons

Weights & Biases Addons is a repository consisting of additional unitilities and community contributions for supercharging your Weights & Biases workflows.