TheWalnut.io: An Easy Way to Create Algorithm Visualizations
Google's DeepDream project has gone viral which allows to visualize the deep learning neural networks. It highlights a need for a generalized algorithm visualization tool, in this post we introduce to you one such effort.
on Jul 29, 2015 in Algorithms, Data Visualization, Javascript, Python
Interview: Thanigai Vellore, Art.com on Delivering Contextually Relevant Search Experience
We discuss the role of Analytics at Art.com, the polyglot data architecture at Art.com, the use cases for Hadoop, vendor selection, supporting semantic search and experience with Avro.
on Jul 23, 2015 in Architecture, Art.com, Avro, Hadoop, HBase, Interview, Semantic Analysis, Solr, Thanigai Vellore
Book: Healthcare Data Analytics
Written by prominent researchers and experts working in the healthcare domain, this book provides a clear understanding of the analytical techniques currently available to solve healthcare problems.
on Jul 20, 2015 in Book, Charu Aggarwal, Healthcare
Top June stories: Top 20 Python Machine Learning Projects; Which Big Data, Data Mining Tools go together?
Top 20 Python Machine Learning Open Source Projects; Which Big Data, Data Mining, and Data Science Tools go together?; Popular Deep Learning Tools - a review; Why Does Deep Learning Work?
on Jul 18, 2015 in Top stories
Deep Learning Adversarial Examples – Clarifying Misconceptions
Google scientist clarifies misconceptions and myths around Deep Learning Adversarial Examples, including: they do not occur in practice, Deep Learning is more vulnerable to them, they can be easily solved, and human brains make similar mistakes.
on Jul 15, 2015 in Adversarial, Deep Learning, Ian Goodfellow, Myths, Regularization
50+ Data Science and Machine Learning Cheat Sheets
Gear up to speed and have Data Science & Data Mining concepts and commands handy with these cheatsheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark and Machine learning algorithms.
on Jul 14, 2015 in Cheat Sheet, Data Science, Django, Hadoop, Machine Learning, Python, R
How to properly present a Data Mining project?
Building models and getting insights are job half done for the data scientist, presenting them to the audience is an art itself. See, how to approach the presentation after wrapping up the data science project.
on Jul 14, 2015 in Algolytics, Data Preparation, Presentation
Can Deep Learning Help you Find the Perfect Girl? – Part 2
Using Deep Learning to find the perfect match, PhD student Harm de Vries describes the process of data collection and analysis. Finally, the results from matching algorithm are compared to human assessment for identifying an individual's dating preferences.
on Jul 13, 2015 in Deep Learning, Love, OkCupid, Online Dating, Predictive Analytics
Can deep learning help find the perfect date?
When a Machine Learning PhD student at University of Montreal starts using Tinder, he soon realises that something is missing in the dating app - the ability to predict to which girls he is attracted. Harm de Vries applies Deep Learning to assist in the pursuit of the perfect match.
on Jul 10, 2015 in Deep Learning, ICML, Love, Machine Learning, Online Dating, Predictive Analytics
Emacs for Data Science
Data science nowadays demands a polyglot developer and, choosing a correct code editor would definitely be a worthy investment. Here we provide, important features of Emacs and its advantages over other editors.
on Jul 10, 2015 in Data Science Tools, Emacs, R, SQL
Dataiku Data Science Studio – intuitive solution for data professionals
Data Science Studio (DSS) from Dataiku is an intuitive software solution that let data professionals harness the power of big data. The latest version DSS 2.0 brings predictive analytics to a whole new level in terms of collaboration and usability.
on Jul 8, 2015 in Data Science Platform, Dataiku
Deep Learning and the Triumph of Empiricism
Theoretical guarantees are clearly desirable. And yet many of today's best-performing supervised learning algorithms offer none. What explains the gap between theoretical soundness and empirical success?
on Jul 7, 2015 in Big Data, Data Science, Deep Learning, Mathematics, Statistics, Zachary Lipton
Top stories for Jun 28 – Jul 4: Top 20 R packages by popularity; Nine Laws of Data Mining
Top 20 R packages by popularity; Top 20 R Machine Learning and Data Science packages; Nine Laws of Data Mining; The missing D in Data Science.
on Jul 6, 2015 in Top stories
Data Science and Big Data: Two very Different Beasts
Creating artifact from the ore requires the tools, craftmanship and science. Same is the case of big data and data science, here we present the distinguishing factors between the ore and the artifact.
on Jul 6, 2015 in Big Data, Data Science, Sean McClure
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