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daily-sessions

Check this page for daily updates

data

Data and code behind the articles and graphics at FiveThirtyEight

data-analysis

Data Science Using Python

data-science-from-scratch

code for Data Science From Scratch book

dataviz

Data visualization guide for presentations, reports, and dashboards

deep-portfolio-management

Source code for the blog post on the evolution of the asset allocation methods

deep-portfolio-management-reinforcement-learning

This repository presents our work during a project realized in the context of the IEOR 8100 RL Class at Columbia University.

deepfacelab

DeepFaceLab is a tool that utilizes machine learning to replace faces in videos. Includes prebuilt ready to work standalone Windows 7,8,10 binary (look readme.md).

deoldify

A Deep Learning based project for colorizing and restoring old images (and video!)

digital-gardeners

Resources, links, projects, and ideas for gardeners tending their digital notes on the public interwebs

digital_video_introduction

A hands-on introduction to video technology: image, video, codec (av1, vp9, h265) and more (ffmpeg encoding).

dlrm

An implementation of a deep learning recommendation model (DLRM)

docker-python

Kaggle Python docker image

docsify-katex

:symbols: KaTeX support for docsify

drawdata

Draw datasets from within Jupyter.

drawio

Source to www.draw.io

drawio-diagrams

Diagrams for draw.io

dsvpn

A Dead Simple VPN.

dumb-codepen

A simple live editor with very few lines of code.

eagleeye

Stalk your Friends. Find their Instagram, FB and Twitter Profiles using Image Recognition and Reverse Image Search.

ecom_transaction_analysis

The Babylist store offers ~5,000 SKUs that are sold by Babylist to consumers. When an item is not in-stock (e.g., the unit is not on hand at the warehouse to be shipped to the consumer), Babylist refers the user to other retailers where they can purchase the good. Key information around orders of goods from our e-commerce store is contained in 3 tables: Bl_fulfillment_order_items table, Bl_fulfillment_orders table and Bl_fulfillment_order_state table. We care about our e-commerce revenue growth and supporting that growth with the right amount of operational investment. Poor operational execution - carrying the wrong items, the wrong level of inventory, and the timeliness in which we fulfill orders - negatively impacts the customer experience. Please address the following in your presentation: ● How healthy are our sales? ○ What’s our average daily order volume and how has it changed over time? ○ Are there any patterns that emerge from the order volatility? ○ What types of patterns do you notice about types or quantities of items ordered? ○ Analyze orders containing bottles. Are there any patterns/trends that you notice? ○ What is the breakdown of purchases from users vs non-users and how has this changed over time? ● How are we doing from an order fulfillment perspective? ○ How many orders are we shipping out on average? ○ For orders created within the last 30 days what’s the % of orders that shipped (state = ‘shipped’) within 12hrs, 12-24hrs, 24-48hrs, greater than 48hrs? And how does that change over time? ○ Is there a pattern on what is shipping out faster or slower than other items? Once you have had a chance to analyze our data and answer the above questions, come up with 1-3 questions of your own about the health/sustainability of our e-commerce business. Try to answer your own questions using the provided data set. Include your questions, hypotheses, and results in your project presentation

educative.io_courses

this is downloadings of all educative.io free student subscription courses as pdf from GitHub student pack

edx-platform

The Open edX platform, the software that powers edX!

elevatorsaga

The elevator programming game!

energyviz

evillimiter

Tool that limits bandwidth of devices on the same network without access.

excalidraw

Virtual whiteboard for sketching hand-drawn like diagrams

faceswap

Deepfakes Software For All

faker

A library for generating fake data such as names, addresses, and phone numbers.

fashion-mnist

A MNIST-like fashion product database. Benchmark :point_right: