ligandro

ligandro's Stars

• mrdbourke/pytorch-deep-learning

  Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

  Language:Jupyter Notebook10.1k3k

• equitable-equations/youtube

  Materials to support learning with my YouTube channel, https://www.youtube.com/c/EquitableEquations.

  Language:HTML11044

• bookyue/my_books

  5718

• mochen862/full-sql-database-course

  4425

• Friends-of-Tracking-Data-FoTD/LaurieOnTracking

  Laurie's code for Metrica tracking data.

  Language:Python28899

• Ali-Hasan-Khan/Scrape-Whoscored-Event-Data

  Get match event data from whoscored.com

  Language:Jupyter Notebook2910

• QiongzhuLi/Chatty_Expert_GPT

  This is a self-developed repository which can learn from any document or knowledge base that you provide and chat with you via voice.

  Language:Python2

• NielsRogge/Transformers-Tutorials

  This repository contains demos I made with the Transformers library by HuggingFace.

  Language:Jupyter Notebook9k1.4k

• michellexliu/receiptify

  Language:HTML48246

• hadjdeh/football-data-analysis

  Football Data Processing & Visualization

  Language:Jupyter Notebook376

• Muhammadatef/ArsenalFC-Data-Pipeline-Project

  Language:Jupyter Notebook151

• adlihs/streamlit_shot_map

  xG Shot map streamlit app generator

  Language:Python44

• Slothfulwave612/Football-Analytics-With-Python

  A repository for football analytics

  Language:Jupyter Notebook3912

• federicorabanos/futbol-data-visualizacion

  Repositorio con info basica de archivos y documentos para la visualización de datos en fútbol y Python

  Language:Jupyter Notebook4312

• ashishps1/awesome-low-level-design

  Learn Low Level Design (LLD) and prepare for interviews using free resources.

  Language:Java6.5k1.8k

• ashishps1/awesome-behavioral-interviews

  Tips and resources to prepare for Behavioral interviews.

  4.1k690

• Ankita6298/spotify-datapipeline-project

  In this project, we build an ETL(Extract, Transform, Load) pipeline, in which pipeline retrieve data from Spotify using Spotify API, transform it to a desired format, and load it into an AWS data store.

  Language:Jupyter Notebook5

• GoogleCloudPlatform/asl-ml-immersion

  This repos contains notebooks for the Advanced Solutions Lab: ML Immersion

  Language:Jupyter Notebook1.7k1k

• kylesteckler/generative-ai

  Language:Jupyter Notebook2

• marcello-calabrese/pdfchatbotlangchain

  This is a dashboard to help you navigate through your PDF files. You can upload the pdf file, ask questions about it and get answers from the chatbot. You can also get a wordcloud of the most used words in the pdf file and a chart with the top ten keywords.

  Language:Python4

• dreji18/Fine-tune-Speech-Recognition

  Tutorial on how to train a custom voice recognition model using Hugging face models.

  Language:Python104

• marcello-calabrese/Webcontentanalysiswebapp

  Creating a Streamlit app that can perform a variety of text analysis tasks, including web scraping, word cloud generation, sentiment analysis, and named entity recognition.

  Language:Python33

• snehankekre/Deploy-Deep-Learning-Models-TF-Serving-Docker

  Official repository of "TensorFlow Serving with Docker for Model Deployment" Coursera Project

  Language:Jupyter Notebook2423

• sankalp1999/Image_Captioning_With_Attention

  CaptionBot : Sequence to Sequence Modelling where Encoder is CNN(Resnet-50) and Decoder is LSTMCell with soft attention mechanism

  Language:Jupyter Notebook487

• alexeygrigorev/data-science-interviews

  Data science interview questions and answers

  Language:HTML8.7k1.9k

• aaronstone1699/Depression-detection

  Depression is one of the most common mental disorders with millions of people suffering from it.It has been found to have an impact on the texts written by the affected masses.In this study our main aim was to utilise tweets to predict the possibility of a user at-risk of depression through the use of Natural Language Processing(NLP) tools and deep learning algorithms.LSTM has been used as a baseline model that resulted in an accuracy of 95.12% and an F1 score of 0.9436. We implemented a hybrid Bi-LSTM + CNN model which we trained on learned embeddings from the tweet dataset was able to improve upon previous works and produce precision and recall of 0.9943 and 0.9988 respectively,giving an F1 score of 0.9971.

  Language:Python194

• Ramsay-m/PortfolioProject1-SQL-Covid-Data

  3

• patrickloeber/python-github-action-template

  Schedule a Python script with GitHub Actions

  Language:Python271251

• EthanRosenthal/discovered-weekly

  Backup each Discover Weekly to playlists

  Language:Python6723

• DataTalksClub/data-engineering-zoomcamp

  Free Data Engineering course!

  Language:Jupyter Notebook24.4k5.2k