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 Project: github.com/milvus-io/milvus

The question answering system is commonly used in the field of natural language processing. It is used to answer questions in the form of natural language and has a wide range of applications. Typical applications include intelligent voice interaction, online customer service,…

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…

Summary:  Looking for your next job in an early stage company but want to make sure your startup has staying power.  Follow the expert rankings by CB Insights that also show us the changing trends in how AI startups should be focusing their offerings.

Let’s suppose you’re early in your data science career and your…

Summary:  We are entering a new phase in the practice of data science, the ‘Code-Free’ era.  Like all major changes this one has not sprung fully grown but the movement is now large enough that its momentum is clear.  Here’s what you need to know.

We are entering a new phase in the practice of data science, the ‘Code-Free’…

There has been much hype surrounding deep learning and data science learning in recent times, and one of the cornerstones of deep learning is the neural network. In this article, we will look at what a neural network is and get familiar with the relevant terminologies.

In simplest terms, a neural network is an interconnection of neurons. Now the question arises, what is a neuron? To understand neurons in deep learning, we first…

Summary:  How about we develop a ML platform that any domain expert can use to build a deep learning model without help from specialist data scientists, in a fraction of the time and cost.  The good news is the folks at the Stanford DAWN project are hard at work on just such a platform and the initial results are extraordinary.

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The second edition (fully revised, extended, and updated) of Machine Learning Algorithms has been published (Packt).

From the back cover:

Machine learning has gained tremendous popularity for…

Summary:  Remember when we used to say data is the new oil.  Not anymore.  Now Training Data is the new oil.  Training data is proving to be the single greatest impediment to the wide adoption and creation of deep learning models.  We’ll discuss current best practice but more importantly new breakthroughs into fully automated image labeling that are proving to be superior even to hand labeling.

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