Machine Learning Archive

DATA SCIENCE

The Ultimate Guide To Natural Language Processing (NLP)

We all hear about Natural language processing and how much impact it has in the state of the art technologies, we have also heard about OpenAI's GPT3 and ChatGPT and how astonishing its performance as if there is an alien behind the scene that understands our questions and give an intelligent answer. But what exactly is NLP and how can we learn it? Here's the ultimate guide to Natural Language Processing (NLP).

Ahmed Raafat

DEEP LEARNING

Graph Neural Networks (GNNs) and it’s Applications

Deep Learning is good at capturing hidden patterns of Euclidean data (images, text, videos). But what about applications where data is generated from non-Euclidean domains, represented as graphs with complex relationships and interdependencies between objects? That’s where Graph Neural Networks (GNN) come in, we’ll explore in this article Graph Neural Network (GNNs) and it's Application

Hesham Ali

DEEP LEARNING

Graphs for Graph Neural Networks

Graphs are a powerful and general representation of data with a wide range of applications. Many data-structures can be represented as graphs. Recently, researchers have introduced the graph data structure use Graphs for Graph Neural Networks (GNN).

Hesham Ali

MACHINE LEARNING

A Beginner’s Guide To Logistic Regression

You may think that Logistic Regression is an algorithm that is used for regression or it is something related to Linear regression. Well, It can be used for regression but actually, it is widely used for classification tasks. It is used to predict categorical variables with the help of dependent variables.

Ahmed Raafat

DEEP LEARNING

Variational Autoencoders: A Vanilla Implementation

Generative models have gained a lot of popularity over the past few years, as their use cases have been growing and it's generally considered a hot research area. The main goal of these models is to generate high quality output data, (e.g. images, texts or sounds) that belong to the same distribution of the input data. Generative models have three main families: Variational autoencoders (VAE), Generative Adversarial Network (GANs) and Diffusion Models. In this article we will focus on the first and the main architecture (VAE), that lead to creating other architectures

Hesham Ali

MACHINE LEARNING

Linear Regression for Continuous Value Prediction

Linear Regression for continuous value prediction is usually the first machine learning algorithm that every data scientist comes across. In brief, It is a very simple model that tries to mimic the behavior of a the dataset using a straight-line.

Ahmed Raafat

DEEP LEARNING

Introduction to GANs: Generative Adversarial Networks

Introduction to GANs: Generative Adversarial Networks How a GAN operates, how to train a GAN network to generate Architectural buildings, and a sample code is included open source.

Yousef Hesham

DEEP LEARNING

What is Evidence Lower Bound (ELBO)?

The evidence lower bound (ELBO) is an important quantity that lies at the core of a number of important algorithms in probabilistic inference such as expectation-maximization and variational inference. To understand these algorithms, it is helpful to understand the ELBO.

Hesham Ali

MACHINE LEARNING

Decision Trees And Random Forests, All You Need To Know

In this article we will go through everything that you need to know about two of the most popular Machine Learning algorithms: Decision Trees and Random Forests, or also known as weak learners and how is it related to a Random Forest.

Ahmed Raafat