M

machine-learning

Machine Learning toolbox for Humans

https://github.com/yandex/rep

It contains all my projects I made in the past 2 years, including smaller programs and bigger applications, from backend to frontend, mainly coded in Java and Python.

Development of a monitoring system that can identify the quality of the printing on the product using the historical vibration data and the quality and production log data.

An Active Learning framework for Drug Discovery with the ability to create targeted models for particular experiments

Word2vec, FastText, GloVE embedding accuracy comparison using analogy, vector-oriented reasoning, synonym and conjugation datasets from multiple sources.

Test project for neural networks - Handwritten digit recognition on MNIST dataset

UMSI-Wikipedia Text Difficulty Prediction (NLP Project)

Capstone 1: Caterpillar Tube Pricing Prediction & Categorization. Capstone 2:Pipeline Multi-Leak Classification.

In MT-BERT we reproduce a neural language understanding model which implements a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple NLU tasks.

A library for Meta-Learning and Few-Shot Learning with PyTorch.

📚 📐 Repository to historically learn about step-by-step mathematics for machine learning.

Dockerized Python microservice with REST API for verbs conjugation in French, Spanish and Portuguese

SecML models and databases zoo.

In SupeRAuGAN we implement a novel data augmentation technique tailored to Generative Adversarial Networks in order to reduce discriminator overfitting and stabilize training

Python course for programmers with a focus on features needed for machine learning

Learn from books in statistics, econometrics, ML and DL. Follows https://trics.me/textbook/index.html, but takes a current selection of books and goes through TOC and code examples.

Delievered at https://epogrebnyak.gitlab.io/statbooks/

Joule is a dataset uploader/hoster/downloader.

Joule.Host | Documentation

Provide a good solution to the CFM 2020 Challenge the hard way (hand writing the optimizer, directly use the Linear Algebra routines, use the most out of the multicore hardware).

Repository containing book material for Introduction to Machine Learning with Python

https://github.com/amueller/introduction_to_ml_with_python https://www.oreilly.com/library/view/introduction-to-machine/9781449369880/ https://www.geeksforgeeks.org/introduction-machine-learning-using-python/