Graph neural networks (GNNs) belong to a category of neural networks that operate naturally on data structured as graphs. Despite being what can be a confusing topic, GNNs can be distilled into just a handful of simple concepts. We'll pick a likely familiar starting point: recurrent neural networks. As you may recall, recurrent neural networks are well-suited to data that are arranged in a sequence, such as time series data or language. The defining feature for a recurrent neural network is that the state of an RNN depends not only on the current inputs but also on the network's previous hidden state.

Humans learn from past experiences, computers learn from previous data.

The article explains the SVM algorithm in an easy way. Machine Learning is considered as a subfield of Artificial Intelligence and it is concerned with the development of techniques and methods which enable the computer to learn. In simple terms development of algorithms which enable the machine to learn and perform tasks and activities. Over a period of time, many techniques and methodologies were developed for machine learning tasks. In this article, we are going to learn almost everything about one such supervised machine learning algorithm which can be used for both classification and regression(SVR) i.e.

This is a follow up to the Introduction to Machine Learning video.

A friendly explanation of how computers predict and generate sequences, based on Recurrent Neural Networks.

This is a follow up to the Introduction to Machine Learning video.