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2015

Public material for CS109

2015lab2

2018-cs109b

Harvard CS109b Public Repository

a-2017

Public Repository for cs109a, 2017 edition

adjectiveintensityordering

Code from paper: "Adjective Intensity Ordering by Representing Word Definitions as a System of Linear Equations"

coding-problems

Solutions for various coding/algorithmic problems and many useful resources for learning algorithms and data structures

colab_playground

computational-linguistics-class.github.io

content

Official content for Harvard CS109

cs231n.github.io

Public facing notes page

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

deep-learning-v2-pytorch

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

deeplearning-models

A collection of various deep learning architectures, models, and tips

gcloud

Google Cloud tutorial and setup

git-and-github

An introduction to git.

interactive-coding-challenges

120+ interactive Python coding interview challenges (algorithms and data structures). Includes Anki flashcards.

intro-spacy-nlp

An introduction to using spaCy for NLP and machine learning

jupyter-notebooks

A collection of Jupyter notebooks.

linear_regression

Implementing linear regression using numpy from scratch

measuring-corporate-culture-using-machine-learning

Code Repository for MS20190155

mit-deep-learning

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

ml_code_from_scratch

project-based-learning

Curated list of project-based tutorials

python-machine-learning-book-3rd-edition

The "Python Machine Learning (3rd edition)" book code repository

python_reference

Useful functions, tutorials, and other Python-related things

spanbert

Code for using and evaluating SpanBERT.

stat479-machine-learning-fs19

Course material for STAT 479: Machine Learning (FS 2019) taught by Sebastian Raschka at University Wisconsin-Madison

tad_19

Text as Data 2019

wine-deep-learning

Exploring applications of deep learning to the world of wine

wineanalysis