Join us as we demystify artificial intelligence without the jargon. This blog transforms technical complexity into clear insights anyone can understand through clear writing, diagrams, and visual guides.
Tokenization is a foundational step in Natural Language Processing (NLP). Whether you’re building a sentiment analysis model, a text classifier, or a large language model (LLM) like GPT, understanding tokenization is essential.
Ever wondered how ChatGPT solves complex problems? Some researchers just released DAPO, an open-source method that’s outperforming the secret sauce from companies like OpenAI and DeepSeek. Let’s break it down
Embeddings are the mathematical foundation that enables machines to understand and work with language. After tokenization converts text to numbers, embeddings transform those numbers into meaningful representations that capture semantic relationships.
Imagine stepping into an arena where two intellectual heavyweights are about to face off in the ultimate reading challenge. The task? Reading and remembering a 200-page novel. One contestant reads it in chunks, forgetting previous sections as they move forward. The other? They retain key points, recall past events, and build a complete understanding of the story.
Ever tried to copy text from a handwritten note or a screenshot? That's why I decided to build something cool using Meta's new Llama 3.2 vision models
Remember how RNNs tend to forget things too quickly? That’s where LSTMs come in! LSTMs are fancy RNNs built to handle sequential data without forgetting important details every few steps.
