A collection of 1 Post
A clear breakdown of the differences and overlaps between agentic AI and software agents, highlighting their behaviors, architectures, and implications for developers building autonomous systems.
A collection of 1 Post
A clear breakdown of the differences and overlaps between agentic AI and software agents, highlighting their behaviors, architectures, and implications for developers building autonomous systems.
A collection of 7 Posts
Explore the essential techniques to clean, transform, and structure raw data before feeding it into machine learning models.
Learn the four pipelines with a practical tour of the end-to-end ML workflow (from data to models to deployment).
A clear breakdown of the differences and overlaps between agentic AI and software agents, highlighting their behaviors, architectures, and implications for developers building autonomous systems.
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.
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.
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.
Ever wondered how your phone predicts the next word while you're texting? That's the magic of **Recurrent Neural Networks (RNNs)** at work! RNNs are like those friends who remember everything you say and use it against you... I mean, use it to make better predictions. 🤖💡
A collection of 2 Posts
Explore the essential techniques to clean, transform, and structure raw data before feeding it into machine learning models.
Learn the four pipelines with a practical tour of the end-to-end ML workflow (from data to models to deployment).
A collection of 1 Post
I’ve compiled this RAG Developer’s Toolkit to help navigate the growing AI ecosystem. Whether you’re working on vector databases, multi-agent systems, or document extraction, this cheat sheet has you covered! 🧠✨
A collection of 2 Posts
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
Encountered a mathematical equation in an image and wished you could extract its LaTeX code instantly? I built a LaTeX OCR tool using GPT-4o and Streamlit.
A collection of 2 Posts
AI agents are intelligent systems designed to autonomously perform tasks, make decisions, or provide insights based on their programmed goals. These agents can operate independently or collaboratively, depending on their design, and are widely used in domains ranging from customer support to autonomous vehicles.
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
A collection of 2 Posts
Explore the essential techniques to clean, transform, and structure raw data before feeding it into machine learning models.
Learn the four pipelines with a practical tour of the end-to-end ML workflow (from data to models to deployment).
