List of articles related to deep learning applied to music

LaTeX/PDF + Epub version of the online book (http://neuralnetworksanddeeplearning.com) ”Neural Networks and Deep Learning“ by Michael Nielsen (@mnielsen)

LATEX: TikZ package for drawing neural networks. Also available on CTAN at http://www.ctan.org/tex-archive/graphics/pgf/contrib/neuralnetwork

List of articles related to deep learning applied to music

Computation of Midcurve of Thin Polygons using Neural Networks

Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent spaces with specified geometry and topology. The manifold latent spaces can be based on analytic expressions or general point cloud representations.

A list of various articles that I find helpful for reading about deep learning, forecasting, or macroeconomics

Course on Language Technologies and NLP

Master Thesis: Image recognition by knowledge transfer using deep convolutional neural network

Some of my more useful LaTeX TikZ digrams

Project Title: Using Machine Learning to Understand the Topology of Knots. This repository contains my Project Log and Code.

In this project, I explore various machine learning techniques including Principal Component Analysis (PCA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Sentiment Analysis in an effort to predict the directional changes in exchange rates for a list of developed and developing countries.

comparative evaluation of incremental machine learning methods

LaTeX source code of the paper and poster of the paper Simplicial Neural Networks

iJazzARTIST: Intelligent Jazz Accompanist for Real-Time human-computer Interactive muSic improvisaTion ( B.Sc. Thesis )

The Intelligent Systems Subteam of Cornell Data Science

Blender/Bullet automatic parameter tuning/learning.

Implementation of AI Poker HULH with Deep CFR and MCCFR ES as thesis for Bachelor of Engineering degree

This repository contains code for a project that trains a neural network to solve solid mechanics problems faster than the traditional finite element method. It includes a pipeline for generating a database of FEM solutions and experiments comparing the neural network model to the FEM.