harbidel Goto Github PK

object-detection-with-tensorflow

Object Detction using Tensorflow Transfer Learning

papers

Curated repository of notes from papers I'm reading, mostly NLP related. Updated regularly.

pidginunmt

Unsupervised Neural Machine Translation from West African Pidgin (Creole) to English without a single parallel sentence

project_kojak

Training a Neural Network to Detect Gestures and Control Smart Home Devices with OpenCV in Python

python

:snake: Python Programs

recommendation-system-for-amazon-content-reviews

research-papers-by-data-science-nigeria

A Collection of Research Papers by Data Science Nigeria

sarcasm-detection

Building a machine Learning Model to Predict Sarcasm

stacked-sack-counting

Counting of Stacked sacks using Computer Vision

summarizeai-app

team-squad

Solving Educational Problems in Nigeria

temperature---resistance-using-neural-network

Predicting Temperature values based on Resistance values using Neural Network

titanic

The historical data has been split into two groups, a 'training set' and a 'test set'. For the training set, we provide the outcome ( 'ground truth' ) for each passenger. You will use this set to build your model to generate predictions for the test set. For each passenger in the test set, you must predict whether or not they survived the sinking ( 0 for deceased, 1 for survived ). Your score is the percentage of passengers you correctly predict. The leaderboard has a public and private component. 50% of your predictions for the test set have been randomly assigned to the public leaderboard ( the same 50% for all users ). Your score on this public portion is what will appear on the leaderboard. At the end of the contest, we will reveal your score on the private 50% of the data, which will determine the final winner. This method prevents users from 'overfitting' to the leaderboard. Data Description Overview The data has been split into two groups: training set (train.csv) test set (test.csv) The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features. The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic. Data Dictionary Variable Definition Key survival Survival 0 = No, 1 = Yes pclass Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd sex Sex Age Age in years sibsp # of siblings / spouses aboard the Titanic

tutorials

Code for all my tutorials

variational-autoencoder

Variational AutoEncoder

web-app-with-langchain-openai-streamlit

Building a demo Web App with LangChain + OpenAI + Streamlit

web-scraping

Web Scaping with Beautiful Soup