Disclaimer: This article requires a basic understanding of neural networks and how fully connected layers function. Convolutional neural networks (CNNs) are a type of network architecture that is commonly used in computer vision tasks. The convolutional layers provide several advantages over fully connected layers, which are commonly used in classical neural network architectures. The main functionality of the convolutional layers is to detect features inside the images while reducing the required computational power in comparison to fully connected layers. In fact, convolutional layers are fully connected layers, in which some of the weights are set to zero and some weights are shared between neurons.

PyTorch (first released by Meta AI) is an open-source machine learning framework based on the Torch library. The framework combines the efficient and flexible GPU-accelerated backend libraries from Torch with an intuitive Python interface, allowing for easy prototyping. Torch is a library for scientific computing with wide support for machine learning algorithms and efficient GPU implementations thanks to C and CUDA. The fundamental work unit of PyTorch is the Tensor: a multi-dimensional matrix containing elements of a single data type, commonly used to store and manipulate the inputs and outputs of a model, as well as the model's parameters. Tensors are similar to NumPy's ndarray, except that tensors can run on GPUs for faster computing.

Knowledge base question answering (KBQA) aims to answer a natural language question over a knowledge base (KB) as its knowledge source. A knowledge base (KB) is a structured database that contains a collection of facts in the form subject, relation, object, where each fact can have properties attached called qualifiers. For example, the sentence "Barack Obama got married to Michelle Obama on 3 October 1992 at Trinity United Church" can be represented by the tuple Barack Obama, Spouse, Michelle Obama, with the qualifiers start time 3 October 1992 and place of marriage Trinity United Church . Popular knowledge bases are DBpedia and WikiData. Early works on KBQA focused on simple question answering, where there's only a single fact involved.

Machine Learning has gradually spread into our lives in different ways. For example, your newly recommended music from Spotify and videos from Netflix. People are talking about machine learning all the time and you might have already hear about it several times in TV shows, news, or even in animations. However you might still be confused about what it really is? Here is a simple quick intro to Machine Learning for non-tech people.

Clustering is the grouping of objects together so that objects belonging in the same group (cluster) are more similar to each other than those in other groups (clusters). In this intro cluster analysis tutorial, we'll check out a few algorithms in Python so you can get a basic understanding of the fundamentals of clustering on a real dataset. For the clustering problem, we will use the famous Zachary's Karate Club dataset. The story behind the data set is quite simple: There was a Karate Club that had an administrator "John A" and an instructor "Mr. Then a conflict arose between them, causing the students (Nodes) to split into two groups.

Clustering is the grouping of objects together so that objects belonging in the same group (cluster) are more similar to each other than those in other groups (clusters). In this intro cluster analysis tutorial, we'll check out a few algorithms in Python so you can get a basic understanding of the fundamentals of clustering on a real dataset. For the clustering problem, we will use the famous Zachary's Karate Club dataset. The story behind the data set is quite simple: There was a Karate Club that had an administrator "John A" and an instructor "Mr. Then a conflict arose between them, causing the students (Nodes) to split into two groups.