&amp;quot;The future is here. It's just not evenly distributed yet.&amp;quot; - William Gibson :::: Follow this topic for fresh resources and ideas related to Data Science, Machine Learning, Algorithms and #bigdata :::: <a href="http://www.dataisbig.co" rel="nofollow">http://www.dataisbig.co</a>/
Thanks to all who attended my webinar earlier this week, Reproducibility with Revolution R Open and the Checkpoint Package. If you missed the live session, you can catch up with the slides and video replay which I've embedded below. If you just want to check out the demo of the checkpoint package, it starts at 18:30 in the video below. If you want to follow along at home, you can download the demo script here. Revolution Analytics webinars: Reproducibility with Revolution R Open and the Checkpoint Package
This post reviews some extremely remarkable results in applying deep neural networks to natural language processing (NLP). In doing so, I hope to make accessible one promising answer as to why deep neural networks work. I think it's a very elegant perspective.http://colah.github.io/posts/2014-07-NLP... ;
R is notoriously a memory heavy language. I don't necessarily think this is a bad thing--R wasn't built to be super performant, it was built for analyzing data! That said, there are times when there are some implementation patterns that are quite...redundant. As an example, I'm going to show you how you can prune a 330 MB glm to 45KB without losing significant functionality.
Let's trim the R fat Le Model Our model is going ...
Our unique data set contains abundant human-curated content, so that Pin, board and user dynamics provide informative features for accurate Pinnability prediction. These features fall into three general categories: Pin features, Pinner features and interaction features:
Pin features capture the intrinsic quality of a Pin, such as historical popularity, Pin freshness and likelihood of spam. Visual features from Convolutional Neural Networks (CNN) are also included. Pinner features are about the particulars of a user, such as how active the Pinner is, gender and board status. Interaction features represent the Pinner’s past interaction with Pins of a similar type.
We, as analysts, specialize in optimization of already optimized processes. As the optimization gets finer, opportunity to make the process better gets thinner. One of the predictive modeling technique used frequently use is regression (Linear or Logistic). Another equally competing technique (typically considered as a challenger) is Decision tree.
Algorithmia, the startup that raised $2.4 million last August to connect academics building powerful algorithms and the app developers who could put them to use, just brought its marketplace out of private beta.
In engineering, there are various ways to build a key-value storage, and each design makes a different set of assumptions about the usage pattern. In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data.
When dealing with small amounts of data, it’s reasonable to try as many algorithms as possible and to pick the best one since the cost of experimentation is low. But as we hit “big data”, it pays off to analyze the data upfront and then design the modeling pipeline (pre-processing, modeling, optimization algorithm, evaluation, productionization) accordingly.
Accurate and timely forecast in retail business drives success. It is an essential enabler of supply and inventory planning, product pricing, promotion, and placement. As part of Azure ML offering, Microsoft provides a template letting data scientists easily build and deploy a retail forecasting solution.
I have compiled notes for all 9 courses of the Johns Hopkins University/Coursera Data Science Specialization. The notes are all written in R Markdown format and encompass all concepts covered in class, as well as additional examples I have compiled from lecture, my own exploration, StackOverflow, and Khan Academy.
These documents are intended to be comprehensive sources of reference for future use and they have served me wonderfully in completing the assignments for each course. So I hope you will find them helpful as well.
Machine Learning from Stanford University. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.
The on demand format allows you to work through the materials at your own pace. All materials are available at any time, and there are no deadlines for exercises or assignments.
If you joined Machine Learning in a previous session but didn’t quite complete the coursework, I hope you’ll consider revisiting the materials in the on demand format. You can now take as much time as you need to fully understand each lesson and complete each assignment successfully.
Capgemini’s 2015 survey of 1,000 senior decision-makers across nine industries and 10 countries revealed that some 43% of organizations are restructuring to exploit data opportunities. Encouragingly, 33% of the surveyed companies have appointed a Chief Data Officer (CDO) or a similar C-level role to lead and exploit such data opportunities, with another 19% planning to do so over the next 12 months.
A new R package available on GitHub from a group of Stanford researchers has the potential to significantly advance the practice of collaborative computing with large data sets distributed over separate sites that may be unwilling to explicitly share data. The fundamental idea is to be able to rapidly set up a web service based on Shiny and opencpu technology that manages and performs a series of master / slave computations which require sharing only intermediate results.
The particular target application for distcomp is any group of medical researchers who would like to fit a statistical model using the data from several data sets, but face daunting difficulties with data aggregation or are constrained by privacy concerns. Distcomp and its methodology, however, ought to be of interest to any organization with data spread across multiple heterogeneous database environments.
A Case Studies Approach to Computational Reasoning and Problem Solving Deborah Nolan, Duncan Temple Lang April 2, 2015 Forthcoming by Chapman and Hall/CRC
Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions.
Sharing your scoops to your social media accounts is a must to distribute your curated content. Not only will it drive traffic and leads through your content, but it will help show your expertise with your followers.
How to integrate my topics' content to my website?
Integrating your curated content to your website or blog will allow you to increase your website visitors’ engagement, boost SEO and acquire new visitors. By redirecting your social media traffic to your website, Scoop.it will also help you generate more qualified traffic and leads from your curation work.
Distributing your curated content through a newsletter is a great way to nurture and engage your email subscribers will developing your traffic and visibility.
Creating engaging newsletters with your curated content is really easy.