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Home » Uncategorized#DevOps for Machine Learning (#ML / #AI) LauraEdellFebruary 2, 2019 at 7:35 am Until very recently, most organizations have seen two distinct, non-overlapping work streams when building an AI enabled application: a development path and a data science path. Often, both groups are actually building similarly scripted functional solutions using something like python or C/F#. Furth

The Economics of “Do More With Less”: Blending AI with Organizational Discipline Bill Schmarzo | September 15, 2024 at 8:14 am I hear it in nearly every customer conversation: “We must find a way to do more with less.” This modern busi... How AI is transforming marketing strategies John Lee | September 12, 2024 at 2:34 pm In 2024, artificial intelligence (AI) will likely transform the marketing in

Home » Uncategorized40+ Modern Tutorials Covering All Aspects of Machine Learning CapriGranville733December 10, 2019 at 3:30 am This list of lists contains books, notebooks, presentations, cheat sheets, and tutorials covering all aspects of data science, machine learning, deep learning, statistics, math, and more, with most documents featuring Python or R code and numerous illustrations or case st

Home » UncategorizedAutomated Text Classification Using Machine Learning ShashankGupta760January 16, 2018 at 6:30 pm Digitization has changed the way we process and analyze information. There is an exponential increase in online availability of information. From web pages to emails, science journals, e-books, learning content, news and social media are all full of textual data. The idea is to crea

Home » UncategorizedFree Book: Foundations of Data Science (from Microsoft Research Lab) CapriGranville733May 23, 2019 at 5:00 am By Avrim Blum, John Hopcroft, and Ravindran Kannan (2018). Computer science as an academic discipline began in the 1960s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical

Home » UncategorizedNew Data Science Cheat Sheet, by Maverick Lin CapriGranville733February 26, 2019 at 2:30 pm Below is an extract of a 10-page cheat sheet about data science, compiled by Maverick Lin. This cheatsheet is currently a reference in data science that covers basic concepts in probability, statistics, statistical learning, machine learning, deep learning, big data frameworks and SQL. T

Home » Uncategorized20 Cheat Sheets: Python, ML, Data Science, R, and More Vincent GranvilleNovember 11, 2018 at 4:00 am This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental

Home » UncategorizedGoogle releases massive visual databases for machine learning EmmanuelleRieufJanuary 2, 2017 at 5:00 am This article was written by Richard Lawler. Richard’s been tech obsessed since first laying hands on an Atari joystick. It seems like we hear about a new breakthrough using machine learning nearly every day, but it’s not easy. In order to fine-tune algorithms that recognize a

Home » UncategorizedBook: Python Data Science Handbook EmmanuelleRieufJuly 1, 2017 at 5:00 am Jupyter notebook content for my OReilly book, the Python Data Science Handbook. This repository contains the full listing of IPython notebooks used to create the book, including all text and code. The code was written and tested with Python 3.5, though most (but not all) snippets will work correctly in Py

Home » UncategorizedCommon Probability Distributions: The Data Scientist’s Crib Sheet EmmanuelleRieufJanuary 22, 2017 at 6:30 am This post was written by Sean Owen. Data scientists have hundreds of probability distributions from which to choose. Where to start? Data science, whatever it may be, remains a big deal.  “A data scientist is better at statistics than any software engineer,” you may over

Home » Uncategorized140 Machine Learning Formulas Vincent GranvilleJanuary 25, 2017 at 1:30 pm By Rubens Zimbres. Rubens is a Data Scientist, PhD in Business Administration, developing Machine Learning, Deep Learning, NLP and AI models using R, Python and Wolfram Mathematica. Click here to check his Github page. Extract from the PDF document This is a 17 page PDF document featuring a collection of

Home » Uncategorized40 Techniques Used by Data Scientists Vincent GranvilleApril 7, 2016 at 6:00 am These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection of articles related to t

Home » Uncategorized12 Algorithms Every Data Scientist Should Know EmmanuelleRieufSeptember 6, 2016 at 5:30 am A rather comprehensive list of algorithms can be found here. Many are posted and available for free on Github or Stackexchange. Algoritmia provides developers with over 800 algorithms, though you have to pay a fee to access them. You can find the original article, here. For other articles

Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Python, SAS Here we ask you to identify which tool was used to produce the following 18 charts: 4 were done with R, 3 with SPSS, 5 with Excel, 2 with Tableau, 1 with Matlab, 1 with Python, 1 with SAS, and 1 with JavaScript. The solution, including for each chart a link to the webpage where it is explained in detail (many times with sou

Home » Uncategorized15 Deep Learning Libraries TeglorSeptember 9, 2015 at 4:30 pm Here are 15 libraries in various languages to help implement your deep learning algorithm. Theano is a python library for defining and evaluating mathematical expressions with numerical arrays. Theano supports related frameworks such as Keras, Pylearn2, Lasagne & Blocks. Caffe is a deep learning framework made with e

Many data set resources have been published on DSC, both big and little data. Some associated with our data science apprenticeship. A list can be found here. Below is a repository published on Github, originally posted here. Source for picture: click here Agriculture U.S. Department of Agriculture's PLANTS Database Biology 1000 Genomes Collaborative Research in Computational Neuroscience (CRCNS) G

Geometric deep learning: AI beyond text & images Kevin Vu | April 23, 2025 at 10:22 am Discover how Geometric Deep Learning revolutionizes AI by processing complex, non-Euclidean data structures, enabling br... What’s wrong with data-based decision-making? Tania Kalambet | April 23, 2025 at 10:11 am Whether launching a new product or changing an existing one, decision-makers are relying on data mo

HomeGreat list of resources: data science, visualization, machine learning, big data Vincent GranvilleDecember 17, 2016 at 6:00 pm Fantastic resource created by Andrea Motosi. I’ve only included the 5 categories that are the most relevant to our audience, though it has 31 categories total, including a few on distributed systems and Hadoop. Click here to view the 31 categories. You might also want

New Book: 0 and 1 – From Elemental Math to Quantum AI Vincent Granville | May 11, 2025 at 11:41 pm This book opens up new research areas in theoretical and computational number theory, numerical approximation, dynamical... The power of distributed data management for edge computing architectures Edward Nick | May 6, 2025 at 9:37 am A profound transformation occurs as organizations generate unprece