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Akshay 🚀

Jul 26, 2023 • 6 tweets • 3 min read • Read on X

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Microsoft is offering FREE courses in following areas:

- AI
- IOT
- Data Science
- Machine Learning

A project-based pedagogy that allows you to learn while building! 🚀

Read More ...👇

1️⃣ AI for beginners

A 12-week, 24-lesson curriculum all about Artificial Intelligence.

You'll learn about:
- Neural Nets
- Deep Learning
- Knowledge representation
- Genetic Algorithms & multi-agent systems

Check this out 👇
https://t.co/rZ3Jdw2Le8microsoft.github.io/AI-For-Beginne…

2️⃣ IOT

Learn about IOT by doing project that cover the journey of food from farm to table.

This includes farming, logistics, manufacturing, retail and consumer - all popular industry areas for IoT devices.

Check this out 👇
https://t.co/o4GJM8gHzmmicrosoft.github.io/IoT-For-Beginn…

3️⃣ Machine Learning

A great course on classical machine learning, using Scikit-learn!

Check this out👇 https://t.co/vpUIczCZ5Tmicrosoft.github.io/ML-For-Beginne…

4️⃣ Data Science

This course covers Deep Learning & Data Science in more details!

Each lesson includes:
- hands-on assignments
- pre & post-lesson quizzes
- instructions to complete the lesson & solutions

Check this out👇
https://t.co/YmOsSEWr7Cmicrosoft.github.io/Data-Science-F…

That's a wrap!

If you interested in:

- Python 🐍
- Machine Learning 🤖
- Maths for ML 🧮
- MLOps 🛠
- CV/NLP 🗣
- LLMs 🧠

Find me → @akshay_pachaar ✔️

Subscribe to my Newsletter→

Everyday, I share tutorials on above topics!

Cheers!mlspring.beehiiv.com/subscribe

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More from @akshay_pachaar

Akshay 🚀

@akshay_pachaar

Apr 26

Let's build an MCP-powered multi-agent deep researcher (100% local):

Before we dive in, here's a quick demo of what we're building!

Tech stack:

- @Linkup_platform for deep web research
- @crewAIInc for multi-agent orchestration
- @Ollama to locally server DeepSeek
- @cursor_ai as MCP host

Let's go! 🚀

System overview:

- User submits a query
- Web search agent runs deep web search via Linkup
- Research analyst verifies and deduplicates results
- Technical writer crafts a coherent response with citations

Now, let's dive into the code!

Read 16 tweets

Akshay 🚀

@akshay_pachaar

Apr 21

Transformer vs. Mixture of Experts in LLMs, clearly explained (with visuals):

Mixture of Experts (MoE) is a popular architecture that uses different "experts" to improve Transformer models.

The visual below explains how they differ from Transformers.

Let's dive in to learn more about MoE!

Transformer and MoE differ in the decoder block:

- Transformer uses a feed-forward network.
- MoE uses experts, which are feed-forward networks but smaller compared to that in Transformer.

During inference, a subset of experts are selected. This makes inference faster in MoE.

Read 10 tweets

Akshay 🚀

@akshay_pachaar

Apr 17

10 MCP, AI Agents, and RAG projects for AI Engineers (with code):

https://x.com/akshay_pachaar/status/1902337037682065797

1️⃣ Real-time Voice RAG Agent

In this project you'll learn how to build a real-time Voice RAG Agent.

You will also learn how to clone your voice in just 5 seconds.

Check the full breakdown (with code) below 👇

https://x.com/akshay_pachaar/status/1902337037682065797

2️⃣ MCP powered Agentic RAG

In this project you'll learn how to build your own MCP server!

It connects to Cursor & performs deep web searches, as well as RAG over a specified directory.

100% open-source code with a step-by-step guide:

Read 12 tweets

Akshay 🚀

@akshay_pachaar

Apr 15

MCP vs A2A (Agent2Agent) protocol, clearly explained:

Agentic applications require both A2A and MCP.

- MCP provides agents with access to tools.
- While A2A allows agents to connect with other agents and collaborate in teams.

Today, I'll clearly explain what A2A is and how it can work with MCP.

What is A2A?

A2A (Agent-to-Agent) enables multiple AI agents to work together on tasks without directly sharing their internal memory, thoughts, or tools.

Instead, they communicate by exchanging context, task updates, instructions, and data.

Read 8 tweets

Akshay 🚀

@akshay_pachaar

Apr 13

Traditional RAG vs. Graph RAG, clearly explained (with visuals):

top-k retrieval in RAG rarely works.

Imagine you want to summarize a biography where each chapter details a specific accomplishment of an individual.

Traditional RAG struggles because it retrieves only top-k chunks while it needs the entire context.

Graph RAG solves this by:

- Building a graph with entities and relationships from docs.
- Traversing the graph for context retrieval.
- Sending the entire context to the LLM for a response.

The visual shows how its different from naive RAG:

Read 8 tweets

Akshay 🚀

@akshay_pachaar

Apr 11

MCP is on fire.

AI agents can now talk to real world tools, apps and actually get stuff done.

This changes everything.

Here are 10 amazing examples:

1️⃣ WhatsApp MCP

Exchange images, videos, and voice notes on WhatsApp!

Pair it with the ElevenLabs MCP server for AI-powered transcription & audio messages with 3,000+ voices.

Check this out👇

2️⃣ MCP-powered Agentic RAG

I created this server for Cursor and lets it perform deep web searches, as well as RAG over a specified directory.

Everything from the comforts of your IDE:

Read 12 tweets

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