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slicerradiomics

A Slicer extension to provide a GUI around pyradiomics

socialmedia_profiling

sort

Simple, online, and realtime tracking of multiple objects in a video sequence.

sota-medseg

SOTA medical image segmentation methods based on various challenges

spectformers

speech-python-demos

pyttsx3 is a text-to-speech conversion library in Python. Its a Python-based alternative for text-to-speech conversion that works offline.

sportclassification

sports-image-classification

Built a image classification model and Streamlit web app to predict the sport that is being represented in an image.

squeeze-unet

Squeeze-unet Semantic Segmentation for embedded devices

stable-diffusion-free-gpu

Stable Diffusion with free Google Colab GPU

staingan

stock-market-analysis-and-price-prediction

This project uses XGBoost and LSTM to forecast stock market performance.

stock-market-forecasting

Forecasting directional movements of stock prices for intraday trading using LSTM and random forest

stock-market-prediction-2020

This is the the final project of the course: L330 Data Science: principles and practice at the University Of Cambridge. The task for this project is stock market prediction using a diverse set of variables. In particular, given a dataset representing days of trading in the NASDAQ Composite stock market, our aim is to predict the daily movement of the market up or down conditioned on the values of the features in the dataset over the previous N (trading) days.

stock-prediction-using-different-models

A collection of notebooks and different prediction models that can predict the stock prices. Also a comparison of how all these models performed.

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

stocks-price-prediction-using-multivariate-analysis

stockstats

Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.

streamlit-example

Example Streamlit app that you can fork to test out share.streamlit.io

streamlit-example-driven

streamlit presentation

streamlit-geospatial

A multi-page streamlit app for geospatial

streamlit-text-summarizer

Generate summaries of news articles or blog posts using HuggingFace's API

streamlit_apps

streamlit_app_1

streamlit_app_3

streamlit_opencv_app

styled-stackgan

WIP CS 230 / CS 224N project: StackGAN conditioned on an image style in addition to the text description

stylegan2

StyleGAN2 - Official TensorFlow Implementation

stylegan2-ada

StyleGAN2 with adaptive discriminator augmentation (ADA) - Official TensorFlow implementation

subject-diffusion

Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning