We all experience video streaming issues, probably on a daily basis. But now that people aren’t just streaming for entertainment, but are relying on it in many cases to perform their jobs, it ups the ante to address some of these issues and provide an improved experience. Deep…

Meet two text mining experts in today’s interview, which explores some of the common issues faced by data scientists in text analytics. Prof. Dursun Delen and…

Predict Number of Active Cases by Covid-19 Pandemic based on Medical Facilities (Volume of Testing, ICU beds, Ventilators, Isolation Units, etc) using Multi-variate…

As its name suggests, searching for videos by image is the process of retrieving from the repository videos containing similar frames to the input image. One of the key steps is to turn…

If you were to invest in real estate, what would be the most important factor you’d take into account? Would it be the age of the building, its location, or maybe how many owners it previously had?

While all of the above will certainly be important to you (though to varying extents), there’s one universal factor that will either be a deal-breaker or make you want to go all in.

You’ve guessed it — the price.

We all have a rough overview of…

Dropout means to drop out units which are covered up and noticeable in a neural network. Dropout is a staggeringly in vogue method to overcome overfitting in neural networks.

Deep Learning framework is now getting further and more profound. With these bigger networks, we can accomplish better prediction exactness. However, this was not the case a few years ago. Deep…

Milvus aims to achieve efficient similarity search and analytics for massive-scale vectors. A standalone Milvus instance can easily handle vector search for billion-scale vectors. However, for 10 billion, 100 billion, or even larger datasets, a Milvus cluster is needed. The cluster can be used as a standalone instance for upper-level applications and can meet the business needs of low latency, high concurrency for massive-scale data. A Milvus cluster can resend requests, separate reading…

With the continuous development of network technology and the ever-expanding scale of e-commerce, the number and variety of goods grow rapidly and users need to spend a lot of time to find the goods they want to buy. This is information overload. To solve this problem, the recommendation system came into being.

The recommendation system is a…

That’s correct. 1 server and 10 lines of code are all you need to search among 1 billion images in a few hundred milliseconds. Its ease of use a few lines of code to handle massive-scale reverse image search. Its superior standalone performance satisfies your need for low-latency, real-time searches. Its support for distributed systems and cloud-native…

Brian Huge and I just posted a working paper following six months of…

Quite often, non-technical executives have difficulties understanding what programming, on a very fundamental level, is all about. Because of that knowledge-gap, they tend to hire and overburden experienced data professionals with tasks which they are hopelessly overqualified for. Such as, for example, doing ad-hoc SQL queries on CRM data: "You're the go-to-guy for all things data, and we need the results for the board meeting tomorrow." That's a quite humbling and frustrating…

Summary:  A little history lesson about all the different names by which the field of data science has been called, and why, whatever you call it, it’s all the same thing.

A little reminiscence, or for those of you who are only recently data scientists, a little history lesson. 

Our profession of…

Many executives struggle to make sense of machine learning (ML) and deep learning (DL). Having a pragmatic relationship with technology, executives need to know on a very fundamental level: "What problems do ML & DL try to solve?" A simple, high-level answer to that question is: "It's all about building systems that do certain things better than humans, with as little intervention by humans as possible." That being said, the simplest way to distinguish between ML and its branch DL…

Our model for recognizing specific animals in images is a neural network consisting of multiple layers, and the initial layers are already good at understanding the world in general. So instead of “re-inventing the wheel,” we only need to train the final layers.

I was excited to work on a recent project with one of our partners, Wild Detect, because it aligns with one of our goals at Appsilon — to use data science consulting to aid in the…

Summary:  If you are guiding your company’s digital journey, to what extent should you be advising them to adopt deep learning AI methods versus traditional and mature machine learning techniques.

By now everyone is at least familiar with using AI/ML as a required cornerstone of company strategy.  Frequently…

Summary:  GANs (Generative Adversarial Nets) originally thought to be a tool for inexpensively creating DNN training data have instead become the tools for creating deep fake images.  However the deep fake technology is now finding uses in the commercial world and promises valuable results.

Generative…

Deep Learning is picking momentum in Quantitative Finance, outside the obvious application to the prediction of asset prices (where to my knowledge it is not particularly effective) and spreading into the more serious application area of option pricing and risk management.

These two recent papers clearly demonstrate the benefits of DL as a pricing technology alternative to the classical FDM and Monte-Carlo in certain contexts:…

Summary:  This may be the golden age of deep learning but a lot can be learned by looking at where deep neural nets aren’t working yet.  This can be a guide to calming the hype.  It can also be a roadmap to future opportunities once these barriers are behind us.

We are living in the golden age of deep…