Much of the recent AI revolution has been focused on automation through big data and/or sensors and feedback into neural networks. The resulting applications are highly valuable to businesses and consumers. They improve quality of life by optimizing labor and resources. However, these applications fall short when it comes to handling human reasoning. Much of the rationale behind the operation of these systems are implicitly embedded in the data.…Continue
Added by Sing Koo on June 22, 2017 at 11:30am — No Comments
Datameer, an end-to-end big data analytics platform, is built on Apache Hadoop to perform integration, analysis, and visualization of massive volumes of both structured and unstructured data. It can be rapidly integrated with any data sources such as new and existing data sources to deliver an easy-to-use, cost-effective, and sophisticated solution for big data analytics.
It simplifies data extraction, data transformation, data loading, and…Continue
Added by Raghavan Madabusi on June 19, 2017 at 6:30pm — No Comments
Organizations all over the EU must be aware by now that the Data Protection Act (DPA) will be changed into GDPR (General Data Protection Regulation). Some of these changes might cause some compliance issues but there’s an easy way to avoid any problems, by raising awareness.
The more your staff and…Continue
Added by Ronald van Loon on June 20, 2017 at 7:00am — No Comments
One of the best ways to learn about any topic is start with very fundamental questions like What, Why etc? Good old Socratic method. In this series of articles on data mining, I plan to approach this topic in a similar fashion.
Simply put, Data…Continue
This article contains phrases taken from the machine learning and analysis world. Data scientists and algorithm engineers will feel more comfortable with reading it although it’s targeted at anyone who is interested in some deep data science learnings. It was written by Ella Gati. Ella is fascinated by machine learning and data science and is excited to be making big data valuable.
Hacking applications such as …Continue
Added by Emmanuelle Rieuf on November 10, 2016 at 4:30pm — No Comments
Added by NYC Data Science Academy on July 22, 2016 at 10:00am — No Comments
Guest Blog from Analytics Vidhya
While Deep Learning has shown remarkable success in the area of unstructured data like image classification, text analysis and speech recognition, there is very little literature on Deep Learning performed on structured / relational data. This investigation also focuses on applying Deep Learning on structured data because we are generally more comfortable with structured data than unstructured…Continue
Added by Emmanuelle Rieuf on June 17, 2017 at 9:00pm — No Comments
This article was written by James Le.
It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples…Continue
Added by Emmanuelle Rieuf on June 19, 2017 at 2:30pm — No Comments
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 design, time series, cross-validation, model fitting, dataviz, AI and many more. To keep receiving these articles, …Continue
Added by Vincent Granville on June 21, 2017 at 8:00am — No Comments
Summary: There’s a three way technology race to bring faster, easier, cheaper, and smarter AI. High Performance Computing is available today but so are new commercial versions of actual Quantum computers and Neuromorphic Spiking Neural Nets. These two new entrants are going to revolutionize AI and deep learning starting now.
In this article, a semi-supervised classification algorithm implementation will be described using Markov Chains and Random Walks. We have the following 2D circles dataset (with 1000 points) with only 2 points labeled (as shown in the figure, colored red and blue respectively, for all others the labels are unknown, indicated by the…Continue
Added by Sandipan Dey on June 6, 2017 at 11:00am — No Comments
The following problems appeared in the exercises in the Coursera course Image Processing (by Northwestern University). The following descriptions of the problems are taken directly from the exercises’ descriptions.
The next figure shows the problem statement.…Continue
Added by Sandipan Dey on June 5, 2017 at 11:00pm — No Comments
I attended the interview with Nick Drake, Senior Vice President, Direct to Consumer at T-Mobile and Otto Rosenberger who serves as CMO at the Hostelworld Group at the Adobe Summit. The key take away of the entire session was that customer experience is the beginning and the core of digital transformations – it is where…Continue
Added by Ronald van Loon on June 19, 2017 at 2:00am — No Comments
The below is an example of how sklearn in Python can be used to develop a k-means clustering algorithm.
The purpose of k-means clustering is to be able to partition observations in a dataset into a specific number of clusters in order to aid in analysis of the data. From this perspective, it has particular value from a data visualisation perspective.
This post explains how to:
Summary: To ensure quality in your data science group, make sure you’re enforcing a standard methodology. This includes not only traditional data analytic projects but also our most advanced recommenders, text, image, and language processing, deep learning, and AI projects.
A Little History…Continue
Added by William Vorhies on July 26, 2016 at 9:15am — No Comments
There is a lot of confusion with the definition of graph databases. In my opinion, any definition that avoids any reference to the semantics of nodes and edges or their internal structure…
Added by Athanassios Hatzis on June 17, 2017 at 3:00am — No Comments
Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week.
Upcoming DSC WebinarsContinue
Added by Vincent Granville on June 17, 2017 at 8:30am — No Comments
R language is the world's most widely used programming language for statistical analysis, predictive modeling and data science. It's popularity is claimed in many recent surveys and studies. R programming language is getting powerful day by day as number of supported packages grows. Some of big IT companies such as Microsoft and IBM have also started developing packages on R and offering enterprise version of R.
Added by Deepanshu Bhalla on June 12, 2017 at 12:30am — No Comments
As a student interested in data science, it's not always straightforward to know exactly what you should focus on to get that first data scientist position. Also, being one of the faster-changing career paths, it's not always clear when certain pieces of career advice have become a bit dated or are…Continue
Added by Andrew Smith on June 15, 2017 at 8:30am — No Comments