Machine learning and Artificial Intelligence are becoming hot technologies right now. While most of the available technologies and expertise is centered around supervised and unsupervised techniques, the real AI paradigm as nature present it to us lies int the ability to learn while interacting with the environment. Traditional supervised learning is unrealistic, as no real…
What is a Data Mart a Data mart is defined as a subset of data form a data warehouse (but not only) of an organization that is limited to a specific business unit or group of users. It is a subject-oriented database and is also known as High-Performance Query Structures (HPQS). If implemented correctly,…
In his 2017 book “From bacteria to Bach and back” Daniel Dennett claims that consciousness is not a mysterious entity and is the result of a Darwinian evolution followed by a post Darwinian intelligent design. He presents us with hypothetical creatures resulting from a Darwin process. Each of them uses a Darwinian process on increasingly…
Machine translation (MT), is the process of translating text from one source language into another target language, is one of the most important applications of NLP. MT is a sequence-to-sequence task. There are a lot of them in NLP. We have a sequence of words in one language as an input, and we want to…
Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). IE is a general category which has various sub-categories, such as entity extraction, event extraction, relation extraction, coreference resolution, and entity linking to name a…
Convolutional Neural Network (CNNs), are mainly known for Computer Vision tasks. They, nevertheless, have various applications in text mining. In this post I will provide insights into some of the most popular NLP applications. We have seen that CNN can capture local features and combinations of these features. This makes them primarily suitable for document…
Why NLP is difficult? Text classification is complex, much more complex than image classification for example. The reason, is that nearly all what you need to classify an image is in the image itself. there is no need for an external supposed knowledge. While in text this is not the case. Generally language always refers…
In this series of blog posts, I’ll be exploring the Intelligent Virtual Assistant (IVA) trends and analyzing it from the technological point of view. I‘ll be thinking about what needs to be done and how to achieve this next level of IVAs. To understand what I mean by next level IVA, I will first define…
In a previous article I introduced Convolutional Neural Networks (CNN) in an intuitive way. In this article I will present few of the modern architectures of the CNN. Convolutional networks have been showing exceptional results in image classification more than a decade ago. Here we will review some of standard CNN architectures and the context…
Introduction Convolutional neural networks (also called CNNs or ConvNets) are neural networks used traditionally to classify images, for example to identify faces, objects, street signs, tumors,… They have the ability to recognise any kind of data if they get trained with a suitable dataset. CNNs are not limited to image recognition, they have been applied…
