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…
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…
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 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…
In the last post I introduced Word2vec and presented it using intuitive concepts. in this post I will implement these ideas in python. I will be using the Open American National Corpus (http://www.anc.org/), which consists of roughly 15 million spoken and written words from a variety of sources. Specifically, we will be using the sub…
In this post I will be implementing the Word2vec algorithm with negative sampling from scratch using python. Implementing this from scratch allow to have a better grasp the inner operations of word2vec’s skip-gram and negative sampling approach. For our analysis, we will use the Open American National Corpus (http://www.anc.org/), which consists of roughly 15 million…
Introduction The goal of any classification algorithm is to predict a value c given the value of a vector x of input features. In classification problems, c is a discrete class label, whereas in a regression problem it is a continuous variable. From a probabilistic point of view, the goal is to find the conditional…
In this article I’m going to analyze a marketing database using Principal Component Analysis or PCA. PCA is particularly useful when we are working with data sets that have a lot of variables. These datasets cannot be easily visualized the in its raw format, and hence it is difficult to get a sense of the…
Markov chains is one of the oldest ways to generate quite believable text. Rather than generating text by randomly selecting characters (possible but completely impractical), we use the Markov property that follows the chain of linked events, where what get generated next depends only on what is generated currently. Typically generating text trough, a Markov process…
