Machine Learning Archive

DEEP LEARNING

Understanding Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are a class of deep neural networks, particularly adept at analyzing visual imagery. They are designed to automatically and adaptively learn spatial hierarchies of features from input images. CNNs have revolutionized the field of computer vision and are widely used in tasks such as Image classification, Object detection, & Segmentation.

Ahmed Zakaria

MACHINE LEARNING

Activation Functions: All You Need To Know

Activation functions in machine learning & neural networks are mathematical functions applied to each neuron or node in the network. It determines whether a neuron should be activated by calculating the weighted sum of inputs and applying a nonlinear transformation.

Mahmoud Reda

DEEP LEARNING

Introduction To Deep Learning

Deep learning, a subset of artificial intelligence (AI), deep learning has become a powerful technology driving transformative changes across industries. it mimics the way the human brain processes information, enabling computers to learn from vast amounts of data. Deep learning revolutionized various fields, including computer vision, natural language processing, speech recognition, healthcare, finance, and autonomous vehicles.

Ahmed Zakaria

MACHINE LEARNING

Recommendation Systems: An Overview

Recommendation systems are Machine Learning algorithms that provide personalized suggestions or recommendations to users. It's designed to help users discover and select items such as products, services, movies, music, or content that they are likely to be interested in. Here's an overview of recommender systems.

Nora Yehia

Natural Language Processing

Text Classification & Sentiment Analysis

Text classification holds immense significance in NLP due to its wide range of applications across different fields. It serves as the backbone for various downstream NLP tasks, including sentiment analysis, spam detection, topic categorization, and document organization. By automatically categorizing textual data, text classification algorithms enable efficient information retrieval, content filtering, and knowledge extraction from large corpora.

Ahmed Zakaria

Natural Language Processing

Word2Vec: NLP with Contextual Understanding

With the ambition to make machines understand human language, traditional methods such as one-hot encoding have proven inadequate. These methods, encoding words as sparse vectors with a single '1' and numerous '0s,' fail to capture the inherent complexities and relationships within language. Words lose their contextual essence, and the sheer volume of data becomes overwhelming for models to process efficiently. The need for Word2Vec models and word embeddings arises from this limitation. In NLP, words are not isolated entities; they derive meaning from their context and relationships with other words. Traditional methods struggle to preserve these nuances, limiting the capability of models to understand human language.

Ahmed Zakaria

Natural Language Processing

Your Prompt Engineering Masterclass (Part 1)

Prompt engineering has become a trending topic with the rise of Large Language Models (LLMs), especially with innovations like ChatGPT. This field emerged from the need to integrate LLMs effectively into various user applications. At its core, prompt engineering is about developing the skills and techniques to maximize the potential of LLMs. This enables the models to interact efficiently with users, answering questions and exhibiting logical reasoning.

Yasmin Younis

Natural Language Processing

Understanding Bag of Words Models

Explore the fundamentals of Bag of Words (BoW) models in natural language processing. Understand how words are represented using one-hot vectors and sparse vectors, and discover the strengths and limitations of BoW models in handling text data for neural networks and other NLP applications.

Ahmed Zakaria

DEEP LEARNING

How Neural Networks Learn: Understanding BackPropagation

Introduction Imagine a neural network as a relay race where each runner represents a layer of the network. The goal of the relay race is to deliver the baton (the data) from the start to the finish line (the output) as accurately as possible. Feedforward Pass (The Forward Run) 1- Start of the Race (Input […]

Ahmed Zakaria