The inability of Deep Learning to perform compositional learning is one of the main reasons for Deep Learning's most critical limitations, including the need to feed them tons of data. Compositionality is the algebraic capacity to understand and produce novel combinations from known components (Loula 2018). While the human brain can easily learn compositionally, Neural Networks (NNs) are not able to discover and store skills that are common across problems, and to re-combine them in a hierarchical fashion to solve new challenges (Liška 2018). The human language learning enjoys a good kind of combinatorial explosion -- if a person knows the meaning of "to run" and that of "slowly", she can immediately understand what it means "to run slowly", even if she has never uttered or heard this expression before the human language learning enjoys a good kind of combinatorial explosion -- if a person knows the meaning of "to run" and that of "slowly", she can immediately understand what it means "to run slowly", even if she has never uttered or heard this expression before (Loula 2018). This principle helps to explain how, when acquiring a language, we can quickly bootstrap to a potentially infinite number of expressions from very limited training data (Loula 2018).

Would you train a neural network with random data? Moreover, are massive neural networks just lookup tables or do they truly learn something? Today's episode is about memorisation and generalisation in deep learning, with Stanislaw Jastrzębski. Stan works as post-doc at New York University. I have asked Stan a few questions I was looking answers for a long time.

Among the consequences of big data is a wealth of relevant minutiae that can be used to train machine learning and other models. That often translates into processing-intensive steps required to train models to perform a specific task. In response, technology startups and government-funded university researchers are promoting a new machine intelligence approach called "lifelong" machine learning as a way to accelerate model training. The latest entrant is Neurala, which this week took the wraps off its lifelong deep neural network technology. The Boston-based startup claims its AI approach can slash training time from upwards of 15 hours to as little as 20 seconds by utilizing "incremental learning."

The promise and ultimate goal of artificial Intelligence is to make machine intelligent. With advancement in machine learning, statistical reasoning and pattern recognition, as well as the exponential growth in big data and computing power, AI has become the front and center of technological innovation and business transformation in the second decade of 21 century and beyond. In this respect, AI is perfectly aligned to the goal of business Intelligence, which is to make business more intelligent by augmenting and, in some cases, automating human intelligence. As AI is getting smarter, it is not unreasonable to expect that BI will too. Traditionally, BI, along with data warehousing and big data technologies, provides systems, tools and processes to help companies harness data from disparate sources and turn them into high quality and actionable information to drive competitive advantage.

Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects -- and then react to what they "see."

In this post, we will learn how the non-max suppression algorithm allows us to overcome multiple detections of the same object in an image. Let's go through an example! Let's say we want to detect pedestrians, cars, and motorcycles in this image. If we look at the picture above we can see that there are two cars. Each of these two cars has one midpoint so it should be assigned to just one grid cell which then actually predicts that there is a car in the picture.

Over the years, many activation functions have been introduced by various machine learning researchers. And with so many different activation functions to choose from, aspiring machine learning practitioners might not be able to see the forest for the trees. Although this range of options allows practitioners to train more accurate networks, it also makes it harder to know which one to use. In this post, I'll demonstrate a little research project I did to see how each of the activation functions performs on the MNIST dataset. I'll start by providing a quick overview of the theory behind each function.

Hello, once again this is the second part of the "Demystifying Generative Models" posts so if you haven't read Part 1 yet, I really urge you to do so here. In the previous post, we discussed the differences between discriminative and generative models, took a peek to the fascinating world of probabilities and used that knowledge to develop a working Naive Bayes that generates passwords for us. Now, we will change our methodologies a little bit and explore how Deep Learning can help us when probabilities fail. Assuming that you have the context knowledge, of part 1, I will jump straight to the point. The Multinomial Naive Bayes model that was developed was based on the assumption that each feature is independent of each other and it worked quite well! But let's change the problem formulation slightly and observe how it behaves.

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