This article was written by Sarthak Jain.

 The real world poses challenges like having limited data and having tiny hardware like Mobile Phones and Raspberry Pis which can’t run complex Deep Learning models. This post demonstrates how you can do object detection using a Raspberry Pi. Like cars on a road,…

Here is our selection of featured articles and technical resources posted since Monday.

Resources

• Unsupervised Learning Algorithms in One Picture
• AAD, Backpropagation and Machine Learning in Finance…

Unsupervised learning algorithms are "unsupervised" because you let them run without direct supervision. You feed the data into the algorithm, and the algorithm figures out the patterns.  The following picture shows the differences between three of the most popular unsupervised learning algorithms: Principal Component Analysis,…

Why is data so important?

Despite being about as prevalent as electricity, it can be difficult to adequately explain how critical data is to the modern world. From business operations to tackling the environmental crisis, data is the key to unlocking insight and developing intelligent solutions across every sector. Although Big Data has been in the news for at least a couple of decades, other types of data are now getting air time as well. Open…

What started as a simple digital payment solution has morphed into a phenomenon with impressive valuations. The Blockchain technology has entered into different business verticals and reinvented their traditional processes, alongside providing them solutions to mitigate prevailing challenges. 

One such domain where the scope and use of Blockchain remained unnoticed is Customer…

Datorium today released results from a recent study comparing popular ride-hailing apps Uber and Lyft among US consumers. Among its findings, critical differences were shown between genders, with notable differences between peer recommendations and actual purchases.

Women love Lyft, Men love Uber…

Automatic Adjoint Differentiation (AAD) and back-propagation are key technologies in modern machine learning and finance. It is back-prop that enables deep neural networks to learn to identify faces on photographs in reasonable time. It is AAD that allows financial institutions to compute the risks of complex derivatives books in real time. The two technologies share common roots.

See the AAD book here:…

Running a business is no easy task, for there are far too many that need to be done and managed every single day. And the thing is that a significant chunk of these daily duties involves processes that are not only mundane but also repetitive in nature. It is especially true in the context of documentation and management of similar assets. Nonetheless, they must be tended to fairly regularly, if not every single day, to make sure that the business continues to move as it is intended to be.…

Machine learning is not a new tech development, but the ethical issues artificial intelligence presents are at their most historically pressing moment. There are multiple ethical questions to confront, including the four most prevalent below.

1. Privacy Is The End Of An…

Businesses generate data from different sources such as text files, multimedia forms, Internet of Things (IoT) devices, social media, and, mobile. Data is being used in various domains such as marketing, social media, healthcare, automation, sales, predictive analysis, etc.

However, the simple Business Intelligence (BI) tools cannot process the humongous data. The advanced analytical tools and algorithms for processing and analyzing can help to draw actionable insights out of…

by Hudson Hollister

Detecting which of the federal government’s millions of contracts most likely involve fraud used to require insider access to agencies’ IT systems. Data analytics provides greater efficacy and higher hit rate than traditional investigative methods – and now can even be performed using only public…

Summary:  Contextually intelligent, NLP-based interactive assistants are one of the next big things for AI/ML.  The tech is already here from recommendation engines.  The need to be more efficient and to become AI-augmented in our decision making is now.  Getting the contextual awareness is the hard part.

 …

This post is part of my forthcoming book The Mathematical Foundations of Data Science. Probability is one of the foundations of machine learning (along with linear algebra and optimization). In this post, we discuss the areas where probability theory could apply in machine learning applications. If you want to know more about…

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. To subscribe, follow this link.  

Featured Resources and Technical…

Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2. 

Source: from the Support Vector Machines chapter,…

Another free book to learn Machine Learning. It also comes with a Youtube video series available here. 

Content

• Machine Learning Setup
• k-Nearest Neighbors / Curse of…

Some original and very interesting material is presented here, with possible applications in Fintech. No need for a PhD in math to understand this article: I tried to make the presentation as simple as possible, focusing on high-level results rather than technicalities. Yet, professional statisticians and mathematicians, even academic researchers, will find some deep and fascinating results worth further exploring. Source code and Excel spreadsheets are provided for replication…

Which statistical method you use to compare data sets depends on two main factors: your overall goal and the type of data you have. Parametric data means that you know the underlying distribution (for example, your data might be normally distributed). Non parametric tests are an…

In the early 2000’s, IBM Deep Blue was on the lookout for its next Grand Challenge.  It had achieved an exhilarating win over chess grand master Garry Kasparov in 1997.  But that was a game with deterministic outcomes where superior processing power gave Deep Blue a significant advantage over even a human grand champion.  IBM needed a challenge commiserate with its Artificial Intelligence aspirations, and the wildly popular TV game show…

Originally posted by Zeeshan Usmani in May 2015.

Big Data, Data Sciences, and Predictive Analytics are the talk of the town and it doesn’t matter which town you are referring to, it’s everywhere, from the White House hiring DJ…