Multithreading in Python clearly explained: Ever felt like your Python code could run faster❓

Multithreading might be the solution you're looking for!

Today, I'll simplify it for you in this step-by-step guide.

Let's go! 🚀

Let's build a multi-agent AI news generator using Cohere's new ⌘R 7B: Before we begin, take a look at what we're building!

The app takes a user query, searches the web for it, and turns it into a well-crafted news article, with citations!

Stack:

- @Cohere ultra-fast ⌘R 7B as LLM
- @CrewAIInc for multi-agent orchestration

Let's go! 🚀

Let's build a RAG app using MetaAI's Llama-3.3 (100% local): Before we begin, take a look at what we're about to create!

Here's what you'll learn:

- @Llama_Index for orchestration
- @qdrant_engine to self-host a vector DB
- @Ollama for locally serving Llama-3.3
- @LightningAI for development & hosting

Let's go! 🚀

Decorators in Python, clearly explained: Decorators are one of the most powerful feature of Python! 🔥

However, understanding them can be a bit overwhelming!

Today, I'll clearly explain how decorators work!

Let's go! 🚀

15 cheat sheets you shouldn't miss as a data scientist: 1. Pandas ↔ Polars ↔ SQL ↔ PySpark Translations.

Master data wrangling by understanding how these frameworks relate to one another. Here's your go-to translation guide for smooth transitions!

Let's build a real-time AI voice assistant, step-by-step: Before we start, here's a quick demo of what we're building!

Tech stack:

- @AssemblyAI to convert speech to text in real-time.
- @OpenAI's GPT-4o to generate intelligent responses.
- @elevenlabsio to convert text responses back to speech.

Let's go! 🚀

Let's build a multi-agent financial analyst using Microsoft's Autogen and Llama3-70B: Before we start, here's a demo and walkthrough of what we're building today.

Tech stack:

- Microsoft's Autogen for multi-agent collaboration
- @Qualcomm's Cloud AI 100 ultra for serving Llama 3:70B

Let's go! 🚀

5 open-source agentic frameworks that will give you superpowers as an AI engineer: 1️⃣ Dymamiq

@DynamiqAGI is like a Swiss army knife for AI Engineers, it supports:

- RAG applications.
- Multi agent orchestration
- And managing complex LLM workflows

Everything in one place!✨

GitHub repo: github.com/dynamiq-ai/dyn…

Let's build a PerplexityAI-like personal research assistant using multi-agent orchestration: Before we dive in, let's see what we're about to create!

We'll use:

- @SwarmZeroAI for multi-agent orchestration
- @Serp_api for real-time search
- @FireCrawl_dev for LLM-ready web scraping

Let's go! 🚀

How LLMs understand relative positions of input words, clearly explained: RoPE (Rotary Positional Embeddings) revolutionised the way positional information is encoded in LLMs and it's widely used by models like Llama-3.

Today, I'll clearly explain what they are & how positional embeddings evolved over time.

Let's go! 🚀

10 great Python packages for Data Science not known to many: 1️⃣ CleanLab

You're missing out on a lot if you haven't started using Cleanlab yet!

Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.

It's like a magic wand! 🪄✨

Check this out👇
github.com/cleanlab/clean…

How LLMs work, clearly explained: Before diving into LLMs, we must understand conditional probability.

Let's consider a population of 14 individuals:

- Some of them like Tennis 🎾
- Some like Football ⚽️
- A few like both 🎾 ⚽️
- And few like none

Here's how it looks 👇

Python *args & **kwargs clearly explained: *args allows you to pass a variable number of non-keyword arguments to a function.

It collects all non-keyword arguments passed to the function and stores them as a tuple.

Consider the following example:

Self-attention in transformers, explained as a directed graph: Self-attention is at the heart of transformers, the architecture that led to the LLM revolution that we see today.

In this post, I'll clearly explain self-attention & how it can be thought of as a directed graph.

Read more...👇

Pandas & NumPy return different values of standard deviation! 🐼 🧮

💭 Wondering why❓🤔

Read more...👇 Pandas considers data to be a sample of a larger population, so to obtain an unbiased result, it uses n-1 instead of n as the divisor.

This is referred to as Bessel's correction in statistics.

On the other hand, NumPy makes no such correction

Read more:
en.wikipedia.org/wiki/Bessel's_…

Lambda functions in Python clearly explained: What are lambda functions ?

Simply put, they are small anonymous functions that are defined without a name.

Check out the syntax 👇

Let's build a multi-agent internet research assistant using OpenAI Swarm & Llama 3.2 (100% local): Before we begin, take a look at what we're building!

The app takes a user query, searches the web for it, and turns it into a well-crafted article.

Stack:

- @Ollama for running LLMs locally
- @OpenAI Swarm for multi-agent orchestration
- @Streamlit for the UI

Let's go! 🚀

Cross-encoders Vs Bi-encoders!

When to use what in a RAG app, clearly explained: Information retrieval is a key component in any RAG setup.

Let's delve into how this is done under the hood and explore where Cross/Bi-encoders fit in.

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10 ways to declare type hints in Python, explained with examples: Type hints are incredibly valuable for improving code quality and maintainability.

Today, I'll walk you through 10 must-know principles to declare type hints in just two minutes. ✨

Let's go! 🚀

Simplest guide to building your first neural network using PyTorch: We'll implement and train this neural network step by step, from scratch, using PyTorch!

Along the way, we'll unravel some interesting facts about how neural nets work! ✨

Let's go! 🚀

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Don't use Venn diagrams to understand SQL joins, try this instead: We start by setting up 2 DataFrames to perform merge operations.

• left
• right

A, B, C are the common keys ✅

Check this 👇