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control

Control Systems Toolbox in R

corex_topic

Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx

covid-sanity

Aspires to help the influx of bioRxiv / medRxiv papers on COVID-19

cppcoreguidelines

The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++

create-ml-app

Template Makefile for ML projects in Python.

crispr

CRISPR/Cas9 guide RNA Design

crispr-dav

CRISPR NGS data analysis and visualization pipeline

crispranalyzer

CRISPRAnalyzeR: interactive analysis, annotation and documentation of pooled CRISPR screens

crisprcasfinder

A Perl script allowing to identify CRISPR arrays and associated Cas proteins from DNA sequences

crisprcleanr

Unsupervised correction of gene independent cell responses to CRISPR-cas9

crispresso2

Analysis of deep sequencing data for rapid and intuitive interpretation of genome editing experiments

crispriadesign

cross-lingual-open-ie

MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models

crypten

A framework for Privacy Preserving Machine Learning

d2l-en

Dive into Deep Learning: an interactive deep learning book on Jupyter notebooks, using the NumPy interface.

dash-canvas

An interactive image editing component for Dash

dash-nlp-new

dash-sample-apps

Apps hosted in the Dash Gallery

dash_cytoscape_nlp

Demo app using dash, dash-cytoscape and nlp from CORD-19 data

data_science

Different study cases of handling and analysis data, using python tools.

datascience-sparksfundinvestmentcasestudy

You work for Spark Funds, an asset management company. Spark Funds wants to make investments in a few companies. The CEO of Spark Funds wants to understand the global trends in investments so that she can take the investment decisions effectively. Business and Data Understanding Spark Funds has two minor constraints for investments: It wants to invest between 5 to 15 million USD per round of investment It wants to invest only in English-speaking countries because of the ease of communication with the companies it would invest in For your analysis, consider a country to be English speaking only if English is one of the official languages in that country You may use this list: Click here for a list of countries where English is an official language. These conditions will give you sufficient information for your initial analysis. Before getting to specific questions, let’s understand the problem and the data first. 1. What is the strategy? Spark Funds wants to invest where most other investors are investing. This pattern is often observed among early stage startup investors. 2. Where did we get the data from? We have taken real investment data from crunchbase.com, so the insights you get may be incredibly useful. For this group project, we have divided the data into the following files: You have to use three main data tables for the entire analysis (available for download on the next page): 3. What is Spark Funds’ business objective? The business objectives and goals of data analysis are pretty straightforward. Business objective: The objective is to identify the best sectors, countries, and a suitable investment type for making investments. The overall strategy is to invest where others are investing, implying that the 'best' sectors and countries are the ones 'where most investors are investing'. Goals of data analysis: Your goals are divided into three sub-goals: Investment type analysis: Comparing the typical investment amounts in the venture, seed, angel, private equity etc. so that Spark Funds can choose the type that is best suited for their strategy. Country analysis: Identifying the countries which have been the most heavily invested in the past. These will be Spark Funds’ favourites as well. Sector analysis: Understanding the distribution of investments across the eight main sectors. (Note that we are interested in the eight 'main sectors' provided in the mapping file. The two files — companies and rounds2 — have numerous sub-sector names; hence, you will need to map each sub-sector to its main sector.) 4. How do you approach the case study? What are the deliverables? The entire case study is divided into checkpoints to help you navigate. For each checkpoint, you are advised to fill in the tables into the spreadsheet provided in the download segment. The tables are also mentioned under the 'Results Expected' section after each checkpoint.

deep-learning-with-r-notebooks

R notebooks for the code samples of the book "Deep Learning with R"

deep-reinforcement-learning

Repo for the Deep Reinforcement Learning Nanodegree program

deep-reinforcement-learning-in-stock-trading

Using deep actor-critic model to learn best strategies in pair trading

deep_learning_pe

Private Equity Sector Ranking Project for Deep Learning course

deepcrispr

design-patterns-in-python

A collection of popular design patterns implemented in Python programming language

dfd_edit

Data Flow Diagram Web Editor

distrom

Distributed Multinomial Regression

dividend_strip

Contains data and documentation for paper: "Valuing Private Equity Investments Strip by Strip" with Arpit Gupta and Stijn Van Nieuwerburgh