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30daysofstreamlit

This is Helloworld app from Day 6 of #3oDaysOfStreamlit

azuremlevaluationresults

In this repo i will trying to show the result differnce between Azure ML Studio (Classic) vs the new one which is Azure ML Studio

benchm-ml

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

bth-ml-with-streaming-data

The project is about SteamFlow prediction of a paper mil. The target is to predict how much water needed to make a paper roll.

csvfiles

datascience_stockmarket

estimating stock index values based on economic indicators with R

deployment-flask

diabetes-prediction

electrical_cost_prediction

Home Electrical bill(in SEK) and it's Forecasting using Neural Network I have collected data from MälarEnergi of my home electrical usage and it's cost. Using those data I have used Neural Network on Time series data to find out the forecast of my cost for next 12 months, where we also tested the model with last 4 months original data. I have used R programming Language and it's different packages, like tidyr, dplyr, forecast and dygraphs for visualization and published the visualtization using rpubs.com

featurewiz

Creating and selecting best features automatically using the 'featurewiz' library in python.

fifa18-wage-prediction-aws

Problem description FIFA 18 Complete Player Dataset from Kaggle.com 17k+ players, 70+ attributes extracted from the latest edition of FIFA I am going to create a model from where club owners can find out or suggest the wage of a player while hiring them into the club.

fifa18-wage-prediction-deploy

Predict Wage for FIFA players --To predict the players wage we have collected the data from Kaggle. And we have applied Random Forest regression with hyperparameter tuning.

fifa18_wage_prediction

Using the dataset from Kaggle(the FIFA 18 players data) i would like to find out the best model by comparing three algorithms LinearRegression, Random Forest and XGboost.

flowise

Drag & drop UI to build your customized LLM flow using LangchainJS

flowisechatembed

fw-frontend

gbm-workshop

Code (and other materials) for an introductory talk/workshop on GBMs (developed originally for an R-Ladies Meetup)

gpt-neo

experimenting GPT-Neo

gpt-sw3-streamlit-app

gpt4_vision

Image Content Description with GPT-4 Vision

h2o-3

Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)

h2o-demos

Some H2O machine learning demos.

h2o-tutorials

Tutorials and training material for the H2O Machine Learning Platform

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

heroku-demo

kth-group4-dm2586

As a part of the course we have developed this Multimodal RAG applicaation

langchain-falcon-chainlit-sudarshan

Simple Chat UI using Falcon model, LangChain and Chainlit

localai

:robot: The free, Open Source OpenAI alternative. Self-hosted, community-driven and local-first. Drop-in replacement for OpenAI running on consumer-grade hardware. No GPU required. Runs ggml, gguf, GPTQ, onnx, TF compatible models: llama, llama2, rwkv, whisper, vicuna, koala, cerebras, falcon, dolly, starcoder, and many others

machine-learning-projects

This repository contains mini projects in machine learning with notebook files

mlops-lab

This repository contains sample code, which is used for a MLOps Workshop