Sepideh Hosseinian's Projects
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
this is a rust project
Copilot assisted algorithms and heuristics
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
This is the final project for the data engineering class at Duke University.
ranger-like terminal file manager written in Rust
Time-Series forecasting using Stats models, LightGBM & LSTM
Production-Grade Container Scheduling and Management
This repositary is a combination of different resources lying scattered all over the internet. The reason for making such an repositary is to combine all the valuable resources in a sequential manner, so that it helps every beginners who are in a search of free and structured learning resource for Data Science. For Constant Updates Follow me in Twitter.
Python & JAVA Solutions for Leetcode
Python and C++ implementation of the problems from Clean Code Handbook - LeetCode 50 Common Interview Questions
my leetcode solutions in rust
LeetCode The Hard Way - From Absolute Beginner to Quitter. Join Discord: https://discord.com/invite/Nqm4jJcyBf
Solutions of LeetCode Problems
Write scalable load tests in plain Python ššØ
Kaggle M5 Forecasting - Uncertainty 4th Place Solution
7th placed solution in M5 demand forecasting
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.
Mastering-Python-Design-Patterns-Second-Edition, published by Packt
Mastering Transformers, published by Packt
ml
Source code accompanying O'Reilly book: Machine Learning Design Patterns
12 weeks, 24 lessons, classic Machine Learning for all
Companion repository for the book Building Machine Learning Powered Applications
Sample MLFlow projects
An example MLFlow project
This repository contains a sample project that uses MLflow, an open source platform for managing the end-to-end machine learning lifecycle. MLflow provides tools and APIs for tracking experiments, packaging and deploying models, and serving and monitoring models in production.