mathusuthan Goto Github PK

2018fall

2018spring

aide-memoire

Data Science Cheat Sheets

anlp19

Course repo for Applied Natural Language Processing (Spring 2019)

clean-code-ml

:bathtub: Clean Code concepts adapted for machine learning and data science. Now a free video series 😎 https://bit.ly/2yGDyqT

cookiecutter-ds-template

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

course-gitbook

Full Stack Deep Learning Course

coursera-ds-algorithms

Codes and notes from UCSD's Data Structures and Algorithms Specialization on Coursera

coursera_reinforcement_learning

Coursera Reinforcement Learning Specialization by by University of Alberta & Alberta Machine Intelligence Institute

cs229-ps-2018

My solutions to the problem sets of Stanford cs229, 2018

deep-learning-papers-reading-roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

deep-learning-v2-pytorch

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

deepar

Tensorflow implementation of Amazon DeepAR

deeplearningbook-notes

Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)

dl_topics

List of DL Topics and resources essential for cracking interviews

dlfs_code

Code for the book Deep Learning From Scratch, from O'Reilly September 2019

gluon-nlp

NLP made easy

grokking-system-design

Thanks to lirenTu@scale

instance_level_recognition

The 2nd place solution to the 2020 edition of the Google Landmark Recognition competition

just-blog

Contents for my site https://mathusuthan.com

keras-idiomatic-programmer

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

lessons

📚 Learn ML with clean code, simplified math and illustrative visuals. As you learn, work on interesting projects and share them on https://madewithml.com for the community to discover and learn from!

mit_ocw_linear_algebra_18_06

IPython notebooks on Gilbert Strang's MIT course on linear algebra (18.06)

nn4nlp-code

Code Samples from Neural Networks for NLP

random-forest-importances

Code to compute permutation and drop-column importances in Python scikit-learn random forests

recsys2019

The complete code and notebooks used for the ACM Recommender Systems Challenge 2019

reinforcement-learning-specialization

[Coursera] Reinforcement Learning Specialization by "University of Alberta" & "Alberta Machine Intelligence Institute"

rosetta_recsys2019

The 4th Place Solution to the 2019 ACM Recsys Challenge by Team RosettaAI

samplemod

scala-fun

Functional Programs in Scala