Let’s break down AI, Machine Learning (ML), and Neural Networks in a structured way, covering key concepts, types of ML, and model architectures like Transformers, and their applications.
Large Language Models (LLMs) such as GPT (Generative Pre-trained Transformer) are a class of deep learning models that have revolutionized natural language processing (NLP).
Graphics Processing Units (GPUs) have become the backbone of modern computing, powering everything from gaming to artificial intelligence (AI).
Cryptocurrency has taken the world by storm, evolving from a niche concept into a mainstream financial asset class.
In modern computing, the seamless transfer of data between various hardware components is crucial for maintaining system performance and efficiency.
Machine Learning
is a vast and intricate field that requires an understanding of key concepts from mathematics, statistics, programming, and data science. Let’s go through everything step-by-step, from the fundamental maths to the essential skills required to build ML models.
What is Sampling? Data sampling is a critical statistical analysis technique used in various fields to efficiently analyze and interpret large data sets. It involves selecting a representative subset of data points from a larger population or dataset. The goal is to identify patterns, trends, and insights that reflect the characteristics of the entire…
Partial Differential Equations (PDEs) are fundamental in various fields including physics, engineering, and finance.
What is Data Science? Data Science is an interdisciplinary field that blends various tools, algorithms, machine learning principles, and statistical techniques with the ultimate goal of extracting valuable insights from raw data. The primary focus of data science is to analyze large and complex data sets to uncover patterns, trends, and relationships that can…
What is a Data Lake? A data lake is a centralized repository that stores vast amounts of raw data in its native format. Unlike traditional data warehouses, which require predefined schemas and are optimized for structured data, data lakes store unprocessed data. This approach provides greater flexibility for advanced analytics, real-time data processing, and machine…
Laplace Transforms serve as a powerful method for solving differential equations and analyzing linear systems. In this blog, we will delve into the essential properties of Laplace Transforms, demonstrating how they can make mathematical computations more manageable. We will cover the key properties, include derivations, and show how these can be utilized in mathematical applications….
The large language model (LLM) landscape is rapidly evolving, and two powerful contenders—DeepSeek and ChatGPT—are emerging as core engines in generative AI applications. While they both excel at generating human-like text, answering questions, and powering chatbots, they differ significantly in architecture, training objectives, inference capabilities, and deployment paradigms. Not long ago, I had my first…
The Drama of the Cubic Equation: Rivalries, Betrayals, and Renaissance Mathematics The 16th century was a time of mathematical duels, secret solutions, and bitter rivalries. The quest to solve the cubic equation wasn’t just about algebra—it was about fame, survival, and revenge. 1. Tartaglia: The Stuttering Genius Who Outsmarted His Rivals Niccolò Tartaglia (1500–1557) was…
Course on Large Language Models NOTE: You’re only meant to change code marked with “# TODO:” Table of Contents Setting Up API Key Configuration Connecting to OpenAI API Exploring the API Creating Chat Completions Understanding Completion Parameters Prompt Engineering Crafting Effective Prompts Strategies and Best Practices Advanced Techniques Utilizing Embeddings Function Calling in LLMs Extras…
Algorithm design and analysis form the backbone of computer science and computational theory. Algorithms are step-by-step procedures or formulas for solving problems, and their efficiency determines how well software performs in terms of speed, memory usage, and scalability.
Artificial Neural Networks (ANNs) are one of the most important advancements in the field of machine learning, inspired by the biological neural networks in the human brain.
