Articles about Learning in AI
Natural Language Processing (NLP) has revolutionized the way machines understand human language. But before models can learn from text, they need a way to break it down into smaller, understandable units. This is where tokenization comes in — a critical preprocessing step that transforms raw text into a sequence of meaningful components, or tokens. 🧠 … Read more
Introduction to MCP The Model Context Protocol (MCP) is an open standard for connecting AI assistants (like large language models) to the systems where data and tools live In essence, MCP aims to bridge the gap between isolated AI models and real-world data sources – think of it as a “USB-C for AI applications”, providing … Read more
The recent release of DeepSeek R1 has generated significant buzz in the AI community. While much of the discussion has centered on its performance relative to models like OpenAI’s GPT-4 and Anthropic’s Claude, the real breakthrough lies in the underlying algorithmic innovations that make DeepSeek R1 both highly efficient and cost-effective. This post explores the … Read more
The world of text-to-speech (TTS) has seen incredible advancements, but often these powerful models require hefty hardware like GPUs. But what if you could run a top-tier TTS model locally on your CPU? Enter Kokoro, a game-changing TTS model that delivers impressive results even on resource-constrained devices. Kokoro: Small but Mighty Kokoro stands out for … Read more
Introduction Understanding BM-25: A Powerful Algorithm for Information Retrieval Bm25 is an enhancement of the TF-IDF model that incorporates term frequency saturation and document length normalization to improve retrieval performance. When it comes to search engines and information retrieval, a vital piece of the puzzle is ranking the relevance of documents to a given query. … Read more
Introduction TF-IDF (Term Frequency-Inverse Document Frequency) is a statistical measure used to evaluate the importance of a word in a document relative to a collection of documents (corpus). It combines two metrics: Term Frequency (TF) and Inverse Document Frequency (IDF). The TF-IDF value increases proportionally with the number of times a word appears in the … Read more
Introduction The latest Ollama update makes it easier than ever to run quantized GGUF models directly from Hugging Face on your local machine. With a single command, you can bypass previous limitations, no longer needing a separate model on the Ollama Model Hub. Step-by-Step Guide 1. Install Ollama 2. Select a Model from Hugging Face … Read more
Introduction OpenAI has launched a groundbreaking new feature for ChatGPT: SearchGPT. This innovative tool blends the conversational nature of a chatbot with the vast resources of the internet, potentially changing the way we search for information forever. With SearchGPT, users can ask questions in natural language and receive concise answers, complete with links to relevant … Read more
NotebookLM: An AI-Powered Research Assistant NotebookLM is a research assistant powered by Google’s Gemini 1.5 Pro model. It’s centred around the idea of using sources and then leveraging the power of Gemini to interact with and learn from them. Here are some of the key features that make NotebookLM such a powerful tool: 1. Versatile … Read more
For part 1 refer to this: Unveiling the Secrets Behind ChatGPT – Part 1 (learncodecamp.net) Implementing a Bigram Language Model When diving into the world of natural language processing (NLP) and language modeling, starting with a simple baseline model is essential. It helps establish a foundation to build upon. One of the simplest and most … Read more
