MACHINE LEARNING Introduction to Support Vector Machines (SVMs) Support Vector Machines (SVM) is a supervised machine learning algorithm introduced by Vladimir N. Vapnik and his colleagues in the 1990s. It excels in classification tasks by identifying an optimal hyperplane that maximizes the margin between classes, ensuring robust performance on unseen data. Leveraging the kernel trick, SVM can handle both linear and nonlinear classification, using various kernel functions like linear, polynomial, radial basis function (RBF), and sigmoid to adapt to diverse data patterns Nora Yehia
MACHINE LEARNING Collaborative Filtering Recommendation System Collaborative filtering is a cornerstone of modern recommender systems, harnessing user interactions and preferences to deliver personalized suggestions. Its impact spans industries, transforming how users interact with digital platforms. This article provides evidence of collaborative filtering, from its theoretical foundations to its practical applications, and offers insights into the technology that shapes the way we make digital choices. Nora Yehia
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