mikhailklassen Goto Github PK

abomination

A project to create completely AI-powered music, including lyrics, vocals, and instrumentals.

airflow_tutorial

A project for learning the fundamentals of Apache Airflow.

biaxial-rnn-music-composition

A recurrent neural network designed to generate classical music.

biharmap

A project to map the state of Bihar, India.

binder_mtsw

Testing deploying Jupyter Notebooks to Binder

cnn_mnist

An exercise in implementing the same CNN architecture in both PyTorch and Tensorflow.

color_palette

Some tools for setting up color-palettes in Python

coords

Test

deepjazz

Deep learning driven jazz generation using Keras & Theano!

developer-roadmap

Roadmap to becoming a web developer in 2019

django-emr

A web-based electronic medical records (EMR) system written in Django.

django_dashboard

Minimal sample application to start developing your web project immediately on Django framework.

dotfiles

Collection of my config and run commands files

drivendata_watertable

edx-platform

Open edX, the edX platform that powers http://www.edx.org

equilibrium-propagation

EP

event_radar

Checks Eventbrite for publicly listed events and sends an email when a matching event is found.

facets

Visualizations for machine learning datasets

flash-tools

A collection of tools for working with data from astrophysical simulations using the FLASH AMR code.

generative-ai-for-beginners

12 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

global_heart_health

A data visualization project on trends in heart disease globally.

heroku-buildpack-conda

homebrew-science

Scientific formulae for the Homebrew package manager

interactive_d3_map

ipython-research-notebook

Research notebook using the IPython notebook

kaggle_digit_recognition

Identifies handwritten digits by training a support vector machine on a training sample and then applies the model to some test data. My submission to the Kaggle 'Digit Recognizer' competition.

kaggle_titanic

Trains a random forest model on the passenger manifest data from the Titanic to predict whether a passenger would be among the survivors based on the information about them in the manifest. My submission to the 'Titanic: Machine Learning from Disaster' challenge on Kaggle.

kddcup2010math_learning

Some data science applications on the student mathematics performance data set from the 2010 KDD Cup.

markov

Markov chain text generator, as used for KingJamesProgramming

mcmaster.latex

LaTeX Template Satisfying McMaster Thesis Formatting Requirements