The rise of artificial intelligence and machine learning has led to the development of models that can process and generate human-like text. One of the most prominent in this domain is the Large Language Model (LLM) , such as OpenAI's GPT series and Google's Gemini series. For investors, understanding the intricacies of these models is essential, especially if they're looking to invest in AI-driven companies or technologies. One such intricacy is the concept of "temperatu

The world of finance has never been disconnected from mathematics and computational methods. As markets become more complex, the tools used to analyze them have also evolved. Machine learning, a subset of artificial intelligence, has rapidly made inroads into financial analysis. One of its foundational algorithms, Gradient Descent, stands at the core of many machine learning models including neural networks. Gradient Descent is an optimization algorithm used to minimize some

For many investors, the allure of buying a stock on the decline, commonly referred to as 'catching a falling knife', can be tempting. The...

In data-driven world, the need for high-quality datasets for modeling, training, and analytics has never been greater. As investors grapple with evolving markets, they are increasingly reliant on data to make informed decisions. Enter synthetic data, a potential game-changer that can redefine the landscape of data utilization in investing. This article is about synthetic data, exploring its relevance, creation, benefits, and potential concerns. What is Synthetic Data ? Synth

Neural networks have become an integral part of the modern technology landscape, driving advancements in areas such as image recognition, natural language processing, and even financial modeling. For investors looking to understand the inner workings of these models, one crucial element to grasp is the concept of weights. In this article, we'll look into what weights are, their significance in neural network architectures, and why they matter from an investment perspective. W

One of the most profound and counterintuitive pieces of investment wisdom comes from Charlie Munger, the vice chairman of Berkshire...

In today's digital age, vast amounts of data are generated every second. For investors and businesses alike, making sense of this data is critical. Enter the world of embeddings based retrieval — a powerful method to extract meaningful insights from data, especially text. In this article, we'll look into what embeddings based retrieval is, why it's relevant for investors, and provide concrete examples of its application. What is Embeddings Based Retrieval ? At its core, emb

Stanley Druckenmiller, one of the most successful hedge fund managers in history, once said, “It’s not whether you're right or wrong...

As investors looking into the world of artificial intelligence and machine learning, one term that frequently emerges is "backpropagation." Understanding this concept can provide insights into how many modern ML models function, particularly neural networks . This article aims to demystify backpropagation for investors, offering a clear overview and relevant examples. What is Backpropagation? Backpropagation, short for "backward propagation of errors," is a supervised learnin

In the thrilling world of mergers and acquisitions, the Pac-Man defense stands out as one of the most dramatic and intriguing...

In the world of finance and investment, the term "clawback" is gradually garnering significant attention. This mechanism, which ensures...

Mark to Market (MtM) is a significant accounting method for accurately valuing an investment or asset based on its current market price....

Michael E. Porter, a professor at Harvard Business School, introduced the concept of Five Forces in his landmark 1979 book, "Competitive...

In artificial intelligence , Inverse Reinforcement Learning (IRL) has emerged as a novel approach to understanding the preferences and decisions of intelligent agents. But what does this mean for investors? In this article, we will delve deep into the world of IRL, understand its fundamentals, and explore how it can be a game-changer for investment strategies. What is Inverse Reinforcement Learning? At its core, Reinforcement Learning (RL) is a type of machine learning wher

In the world of business and investments, the term "roll-up" refers to the strategy of acquiring and integrating multiple smaller...

Principal Component Analysis (PCA) is a powerful statistical technique used for dimensionality reduction. It allows researchers,...

In today's fiercely competitive market, businesses must understand their customers deeply to stand out and deliver memorable products....

In artificial intelligence and machine learning , investors are keen to pinpoint the next trend. One of the recent advancements, the Retrieval Augmented Generation (RAG), promises to propel AI models , particularly in the field of natural language processing (NLP). What is Retrieval Augmented Generation (RAG)? Retrieval Augmented Generation is a method that combines the power of large-scale retrievers and sequence-to-sequence generative models. Instead of solely depending

Benjamin Graham , widely recognized as the father of value investing, once astutely observed, "The market is a pendulum that forever swings between unsustainable optimism and unjustified pessimism." This insightful commentary not only captures the cyclical nature of stock markets but also underscores the emotional dynamics that often drive investor behavior. By understanding this continual sway between exuberance and fear, investors can be better equipped to navigate the turb

In the fast evolving world of Artificial Intelligence, two of the most predominant types of machine learning are supervised and unsupervised learning. These paradigms shape the way machines gather knowledge and insights from data. For investors aiming to fund AI-driven startups and companies or leverage AI technologies in their portfolios, understanding the nuances of these methodologies can be instrumental. This article provides an overview of supervised and unsupervised lea