This article focuses on demystifying the difference between traditional data analytics methods vs. machine-learning-driven ones, not without providing firstly a clear understanding of what is — and what is not — data analytics compared to other data terms often used interchangeably. After gaining such understanding, the post provides clear and succinct guidelines on when to […]
Without any doubt, Large Language Models (LLMs) have emerged as one of the biggest AI breakthroughs in recent years: they excel in understanding and generating human-like text, making them versatile for a wide range of applications. Learning LLMs’ foundations and practical nuances may sound daunting to many, but this article is here to help. Keep […]
Unity makes strength. This well-known motto perfectly captures the essence of ensemble methods: one of the most powerful machine learning (ML) approaches -with permission from deep neural networks- to effectively address complex problems predicated on complex data, by combining multiple models for addressing one predictive task. This article describes three common ways to build ensemble […]
Recommender systems enhance user experiences in Internet-based applications by recommending items tailored to individual preferences or needs, such as products, services, or content. Used in various sectors including e-commerce, tourism, and entertainment, these systems stimulate user engagement, and customer loyalty, and can ultimately help increase customer satisfaction and revenue in certain domains like the retail […]
Machine learning (ML) models contain numerous adjustable settings called hyperparameters that control how they learn from data. Unlike model parameters that are learned automatically during training, hyperparameters must be carefully configured by developers to optimize model performance. These settings range from learning rates and network architectures in neural networks to tree depths in decision forests, […]
This article explains, through clear guidelines, how to choose the right machine learning (ML) algorithm or model for different types of real-world and business problems. Knowing to decide on the right ML algorithm is crucial because the success of any ML project depends on the correctness of this choice. The article starts by presenting a […]
Natural language generation (NLG) is an enthralling area of artificial intelligence (AI), or more specifically of natural language processing (NLP), aimed at enabling machines to produce human-like text that drives human-machine communication for problem-solving. This article explores what NLG is, how it works, and how this area has evolved over recent years while underscoring its […]
Check out the previous articles in this series: Understanding RAG Part I: Why It’s Needed Understanding RAG Part II: How Classic RAG Works Having previously introduced what is RAG, why it matters in the context of Large Language Models (LLMs), and what does a classic retriever-generator system for RAG look like, the third post in […]
Computer vision is a branch of Artificial Intelligence (AI) that studies how machines can interpret and understand visual information, such as images and videos. Most computer vision models today are based on deep learning architectures like Convolutional Neural Networks (CNNs), which excel at tasks such as image classification, object detection, and segmentation. However, the necessary […]
In the first post in this series, we introduced retrieval augmented generation (RAG), explaining that it became necessary to expand the capabilities of conventional large language models (LLMs). We also briefly outlining what is the key idea underpinning RAG: retrieving contextually relevant information from external knowledge bases to ensure that LLMs produce accurate and up-to-date […]
