Mikael Yemane's starred repositories
hiring-without-whiteboards
⭐️ Companies that don't have a broken hiring process
DeepLearningExamples
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
pytorch-deep-learning
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
data-science-from-scratch
code for Data Science From Scratch book
Data-Science-Cheatsheet
A helpful 5-page machine learning cheatsheet to assist with exam reviews, interview prep, and anything in-between.
DOOM-3-BFG
Doom 3 BFG Edition
LSTM-Neural-Network-for-Time-Series-Prediction
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data
data-science-question-answer
A repo for data science related questions and answers
SanAndreasUnity
Open source reimplementation of GTA San Andreas game engine in Unity
osgameclones
Open Source Clones of Popular Games
machine-learning-interview-questions
This repository is to prepare for Machine Learning interviews.
open-source-games
A list of open source games.
Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis
Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall
AzureDatabricksBestPractices
Version 1 of Technical Best Practices of Azure Databricks based on real world Customer and Technical SME inputs
Stanford-Project-Predicting-stock-prices-using-a-LSTM-Network
Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).
RNN-stocks-prediction
Another attempt to use Deep-Learning in the financial markets
recipes-regression-template
Template repo for kickstarting recipes for regression use case
recipes-examples
Example repo to kickstart integration with mlflow recipes.
e2e-mlops-azure
Demo repository implementing an end-to-end MLOps workflow on Databricks, using Azure DevOps for CICD orchestration. Project derived from dbx basic python template
recipes-classification-template
Template repo for kickstarting recipes for classification use case