AlexShypula

mt-dnn

Multi-Task Deep Neural Networks for Natural Language Understanding

AlexShypula.github.io

Amazon_Reviews

Analysis of Amazon Reviews Data Set as advisor for Analytics and the Digital Economy Wharton Course

CodeGen

Reference implementation of code generation projects from Facebook AI Research. General toolkit to apply machine learning to code, from dataset creation to model training and evaluation. Comes with pretrained models.

CodeT5

Home of CodeT5: Open Code LLMs for Code Understanding and Generation

CourseraML

deepRL

Code and Experiments from Spinning up in Deep RL

distillation_experiments

MNIST distillation experiment

djangoapp

The polls app from the official Django tutorial, that demonstrates how to build data-driven Python apps in Azure App Service.

NeuralMsgPass

NN4NLP_Classification

Exploring Neural Models for a Classification Task for CMU 11-747 Neural Networks for NLP 2020

itsAlive

joeynmt

Minimalist NMT for educational purposes

Learning-to-Optimize-Real-World-Programs

llvm-clang-samples

UNMAINTAINED: Examples of using the LLVM and Clang compilation libraries and tools

Mask-Align

Code for our paper "Mask-Align: Self-Supervised Neural Word Alignment" in ACL 2021

NeuralMatMul

experiments using neural nets to learn matrix multiplication

preTrainingJoeynmt

derivative of joeynmt for pre-training

program_optim_paper

PyDrive

Google Drive API Python wrapper library

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

socioprophet

Socioprophet is a collaborative platform for econo-finance geeks that enables users to collaborate on algorithms, research, and development

stanford-cs-229-machine-learning

VIP cheatsheets for Stanford's CS 229 Machine Learning

stoke

STOKE: A stochastic superoptimizer and program synthesizer

transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

tranX

A general-purpose neural semantic parser for mapping natural language queries into machine executable code

x86reference

X86 Opcode and Instruction Reference: http://ref.x86asm.net

xml2pytorch

Using xml to define pytorch neural networks