cassiahh

cassiahh's Stars

• YuriCicconi/Desafio-App-Musica

  Repositório criado para armazenar os arquivos do desafio do curso de Java da Alura

  Language:Java2

• Milsondepaz/como-instalar-intellij-no-ubuntu

  154

• facebookresearch/segment-anything

  The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

  Language:Jupyter Notebook49.3k5.8k

• amimaro/Provas-POSCOMP

  Provas e gabaritos da POSCOMP, sem marcação das respostas :books:

  611110

• chucktomasi/sn-learn-javascript

  Example scripts from the series "Learn JavaScript on the Now Platform"

  Language:JavaScript481758

• words-sdsc/coursera

  Data sets and scripts for Coursera Big Data Specialization.

  Language:Jupyter Notebook160367

• ashishpatel26/Applied-AI-with-Deep-learning-By-IBM-Coursera

  Language:Jupyter Notebook1011

• henriquepeixoto/Data-science-cool-stuff

  Things that I've studied and some cool things that I've done :)

  Language:Jupyter Notebook31

• Kumaava/BasicStatistics

  University of Amsterdam

  Language:R4026

• claimed-framework/component-library

  The goal of CLAIMED is to enable low-code/no-code rapid prototyping style programming to seamlessly CI/CD into production.

  Language:Jupyter Notebook2.3k4k

• ProfessorKazarinoff/staticsite

  Repository for my static site build. Uses Python and Pelican, a static site generator

  Language:Jupyter Notebook51

• google-github-actions/setup-gcloud

  A GitHub Action for installing and configuring the gcloud CLI.

  Language:TypeScript1.8k513

• dylanroy/google-cloud-run-github-actions

  A sample project with a Github Action for deploying to Google Cloud Run.

  Language:Dockerfile421

• MarcSkovMadsen/awesome-streamlit

  The purpose of this project is to share knowledge on how awesome Streamlit is and can be

  Language:HTML2.1k355

• fauconnier-n/Streamlit-SHAP-Explorer

  A Streamlit Web Application that predicts the genre of a song, interactively explores the corresponding SHAP values and locally explains a CatBoost Multi Classification model

  Language:Jupyter Notebook6

• ImpulsoGov/streamlitlastversion

  Language:TypeScript1

• cassiahh/TCC

  Language:Jupyter Notebook1

• streamlit/streamlit

  Streamlit — A faster way to build and share data apps.

  Language:Python38.3k3.3k

• pydata/pandas-datareader

  Extract data from a wide range of Internet sources into a pandas DataFrame.

  Language:Python3k681

• streamlit/streamlit-example

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

  Language:Python36426.1k

• google-github-actions/deploy-cloudrun

  A GitHub Action for deploying services to Google Cloud Run.

  Language:TypeScript501118

• postmanlabs/postman-app-support

  Postman is an API platform for building and using APIs. Postman simplifies each step of the API lifecycle and streamlines collaboration so you can create better APIs—faster.

  5.9k859

• kmyrdahl/Stock-Scraper

  Stock scraper/analyzer using Yahoo Finance and Jupyter

  Language:Jupyter Notebook1

• DougOliver12/Python-Bolsa-Yahoo

  Web Scrapping em Python do Yahoo Finance

  Language:Jupyter Notebook2

• var97/Stock-Trend-Prediction

  Predicting Upward and downward trends in the stock prices using Stacked LSTM.

  Language:Python63

• Prajwal10031999/Stock-Price-Prediction-And-Forecasting-Using-Stacked-LSTM

  Stock-Market-Forecasting using DEEP LEARNING

  Language:Jupyter Notebook2213

• 2M-kotb/LSTM-based-Stacked-Autoencoder

  Language:Python186

• planetceres/bitcoin-nn

  Predictive modeling for Bitcoin prices using LSTM and stacked neural nets

  Language:Python249

• deadskull7/New-York-Stock-Exchange-Predictions-RNN-LSTM

  BEST SCORE ON KAGGLE SO FAR. Mean Square Error after repeated tuning 0.00032. Used stacked GRU + LSTM layers with optimized architecture, learning rate and batch size for best model performance. The graphs are self explanatory once you click and go inside !!!

  Language:Jupyter Notebook6938

• aparajitad60/Stacked-LSTM-for-Covid-19-Outbreak-Prediction

  Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, China, and has since spread globally, resulting in an ongoing pandemic. Long Short Term Memories(LSTMs) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDS's (intrusion detection systems). LSTMs can also be efficiently applied for time-series predictions. In this project, its shows a four stacked LSTM network for early prediction new Coronavirus dissease infections in some of the mentioned affected countries (India, USA, Czech Republic and Russia) , which is based on real world data sets which are analyzed using various perspectives like day-wise number of confirmed cases, number of Cured cases, death cases. This attempt has been done to help the concerned authorities to get some early insights into the probable devastation likely to be effected by the deadly pandemic.

  Language:Python4719