Hello Folks🙏, So Today I am Continue my 100 Days Series of ML ✅Day 92 of #MachineLearning 🎰 ▫️ Topic - K-Nearest Neighbors (KNN) #Algorithm in ML #kNN is simplest of all ML algorithms & It is non-parametric algorithm used for classification & regression task A Thread🧵

✅Day 94 of #MachineLearning ▫️ Topic - Naive Bayes algorithm in ML🔧 📝#NaïveBayes classification is a straightforward & powerful algorithm for classification task🧝 A Complete Thread🧵

If you are using jupyter notebooks for Python and Data Science, try these 7 magic commands that will save you a ton of time:🧵 1. Jupyter AI: Select any model and chat with it right from the Jupyter Notebook.

Building Real-World Applications with LLMs! A Free course where you'll learn how to build real-world, production grade LLM applications. It also covers essential topics like fine-tuning, prompt engineering, experiment management, model versioning, deployment, monitoring and

Have you ever wondered how 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗩𝗲𝗰𝘁𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀 (SVM) can handle non-linear data? The "𝗞𝗲𝗿𝗻𝗲𝗹 𝗧𝗿𝗶𝗰𝗸" is a fascinating mathematical technique that allows efficient calculations and delivers powerful results! Let's learn more about it 🧵 👇

Vector databases are pivotal for Large Language Models (LLMs) due to their ability to handle high-dimensional vector data efficiently. ▶️ They optimize storage, retrieval, and management of vector embeddings crucial for LLM performance. Learn more 👇 🧵

PCA implemented from scratch in Python! -- step by step explanation with code--

Microsoft launched the best course on Generative AI! The free 18 lesson course is available on Github and will teach you everything you need to know to start building Generative AI applications.

Introducing Meta Llama 3: the most capable openly available LLM to date. Today we’re releasing 8B & 70B models that deliver on new capabilities such as improved reasoning and set a new state-of-the-art for models of their sizes. Today's release includes the first two Llama 3

Let's build a RAG app using MetaAI's Llama-3 (100% local):

The Top ML Papers of the Week (April 22 - April 28): - Phi-3 - OpenELM - AutoCrawler - Self-Evolution of LLMs - AI-powered Gene Editors - Make Your LLM Fully Utilize the Context ...

Build a LLM app with RAG to chat with YouTube videos in just 30 lines of Python Code (step-by-step instructions):

Llama 3 is insanely moving fast. People are really pushing Llama 3 to its limits in incredible ways. 10 wild examples (and use cases)

Self-attention in transformers clearly explained:

Day 24 of llm.c: we now do multi-GPU training, in bfloat16, with flash attention, directly in ~3000 lines of C/CUDA, and it is FAST! 🚀 We're running ~7% faster than PyTorch nightly, with no asterisks, i.e. this baseline includes all modern & standard bells-and-whistles: mixed

If you are looking for something to code & read this weekend, I uploaded a notebook to finetune a small GPT model to classify SPAM messages with ~96% accuracy: github.com/rasbt/LLMs-fro… (Fun fact: it's small enough to train it on your laptop; ~5 min on my M3 MacBook Air!)

Build a Web Scrapping AI Agent with RAG in just 30 lines of Python Code (step-by-step instructions):

K-Nearest Neighbours (KNN), clearly explained:

Retrieval Augmented Generation (RAGs), clearly explained:

Andrej Karpathy's (Andrej Karpathy) keynote yesterday at AI Startup School in San Francisco.