Akshay ๐Ÿš€ Profile picture
Jul 1, 2023 โ€ข 11 tweets โ€ข 4 min read โ€ข Read on X
Object oriented programming is essential for writing clean & modular code!

Let's clearly understand OOPs with Python! ๐Ÿš€

A Thread ๐Ÿงต๐Ÿ‘‡
We break it down to 6 important concepts:

- Object ๐Ÿš˜
- Class ๐Ÿ—๏ธ
- Inheritance ๐Ÿงฌ
- Encapsulation ๐Ÿ”
- Abstraction ๐ŸŽญ
- Polymorphism ๐ŸŒ€

Let's take them one-by-one... ๐Ÿš€
1๏ธโƒฃ Object ๐Ÿš˜

Just look around, everything you see can be treated as an object.

For instance a Car, Dog, your Laptop are all objects.

An Object can be defined using 2 things:

- Properties: that describe an object
- Behaviour: the functions that an object can perform

...๐Ÿ‘‡
For example, a Car is an object that has properties such as color & model, and behaviours such as accelerating, braking & turning.

But, how do we create these objectsโ“๐Ÿค”

This is where we need to understand Classes!

...๐Ÿ‘‡
2๏ธโƒฃ Class ๐Ÿ—๏ธ

A class is like a blueprint for creating objects.

It defines a set of properties & functions (methods) that will be common to all objects created from the class.

So, we start with a simple example & follow along!

Let's define a class Car & create it's Object๐Ÿ‘‡
3๏ธโƒฃ Inheritance ๐Ÿงฌ

Let's say we want to create an Electric car & don't want to define all the properties and methods of the basic Car class.

Inheritance helps us to inherit all the properties/methods of parent class & add new ones or override existing.

Check this out๐Ÿ‘‡
4๏ธโƒฃ Encapsulation ๐Ÿ”

Encapsulation helps to bundle data and methods inside a class, restricting direct access to certain attributes and methods.

We use private attributes/methods (with a `_` or `__` prefix) to achieve this.

Here's an example ๐Ÿ‘‡
5๏ธโƒฃ Abstraction ๐ŸŽญ

This concept focuses on exposing only essential information to the outside world while hiding implementation details.

We use abstract classes and methods to define a common interface.

Here's an example ๐Ÿ‘‡
At this point if Abstraction and Encapsulation confuse you! ๐Ÿ‘‡

Abstraction conceals the implementation details, but doesn't hide the data itself.

On the other hand, Encapsulation hides the data and restricts unwanted use from external sources.

Cheers! ๐Ÿฅ‚
6๏ธโƒฃ Polymorphism ๐ŸŒ€

This allows us to use a single interface for different data types or classes.

We can achieve this through method overriding, where a subclass provides a different implementation for a method defined in its parent class.

Let's understand with an example ๐Ÿ‘‡
That's a wrap!

If you interested in:

- Python ๐Ÿ
- Data Science ๐Ÿ“ˆ
- Machine Learning ๐Ÿค–
- Maths for ML ๐Ÿงฎ
- MLOps ๐Ÿ› 
- CV/NLP ๐Ÿ—ฃ
- LLMs ๐Ÿง 

I'm sharing daily content over here, follow me โ†’ @akshay_pachaar if you haven't already!

Newletter:

Cheers! ๐Ÿฅ‚mlspring.beehiiv.com

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More from @akshay_pachaar

Jun 28
I have tested 100+ MCP servers in the last 3 months!

Here are 6 must-use MCP servers for all developers (open-source):
1๏ธโƒฃ Graphiti MCP server

Agents forget everything after each task.

Graphiti MCP server lets Agents build and query temporally-aware knowledge graphs, which act as an Agent's memory!

Check this๐Ÿ‘‡
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Check this demo๐Ÿ‘‡
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Let's generate our own LLM fine-tuning dataset (100% local):
Before we begin, here's what we're doing today!

We'll cover:
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Finally, we'll create our own instruction fine-tuning dataset.

Let's dive in!
Once an LLM has been pre-trained, it simply continues the sentence as if it is one long text in a book or an article.

For instance, check this to understand how a pre-trained LLM behaves when prompted ๐Ÿ‘‡ Image
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Jun 22
Let's build a real-time Voice RAG Agent, step-by-step:
Before we begin, here's a quick demo of what we're building

Tech stack:

- @Cartesia_AI for SOTA text-to-speech
- @AssemblyAI for speech-to-text
- @LlamaIndex to power RAG
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Let's go! ๐Ÿš€
Here's an overview of what the app does:

1. Listens to real-time audio
2. Transcribes it via AssemblyAI
3. Uses your docs (via LlamaIndex) to craft an answer
4. Speaks that answer back with Cartesia

Now let's jump into code!
Read 11 tweets
Jun 21
Let's build an MCP-powered audio analysis toolkit:
Before we dive in, here's a demo of what we're building!

Tech stack:
- @AssemblyAI for transcription and audio analysis.
- Claude Desktop as the MCP host.
- @streamlit for the UI

Let's build it!
Here's our workflow:

- User's audio input is sent to AssemblyAI via a local MCP server.
- AssemblyAI transcribes it while providing the summary, speaker labels, sentiment, and topics.
- Post-transcription, the user can also chat with audio.

Let's implement this!
Read 11 tweets
Jun 19
AI agents can finally talk to your frontend!

The AG-UI Protocol bridges the critical gap between AI agents and frontend apps, making human-agent collaboration seamless.

MCP: Agents to tools
A2A: Agents to agents
AG-UI: Agents to users

100% open-source.
Here's the official GitHub repo for @CopilotKit's AG-UI:

(don't forget to star ๐ŸŒŸ)github.com/ag-ui-protocolโ€ฆ
Here's a really good illustration of how it works!

Key features:

๐Ÿค Works with LangGraph, LlamaIndex, Agno, CrewAI & AG2
๐ŸŽฏ Event-based protocol with 16 standard event types
๐Ÿ’ฌ Real-time agentic chat with streaming
๐Ÿง‘โ€๐Ÿ’ป Human-in-the-loop collaboration
๐Ÿ’ฌ ChatUI & Generative UI
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Jun 16
Top 4 open-source LLM finetuning libraries!

From single-GPU โ€œclick-to-tuneโ€ notebooks to trillion-param clusters, these four libraries cover every LLM finetuning scenario.

Understand which one to use, & when...๐Ÿ‘‡ Image
1๏ธโƒฃ Unsloth

Unsloth makes fine-tuning easy and fast, turning a mid-range GPU into a powerhouse with a simple Colab or Kaggle notebook.

Perfect for hackers and small teams using 12โ€“24 GB GPUs needing quick LoRA experiments without DeepSpeed configs or clusters

Check this out๐Ÿ‘‡
github.com/unslothai/unslโ€ฆ
2๏ธโƒฃ Axolotl

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Perfect for teams that crave reproducibility and want to toggle advanced recipes by flipping a YAML switch.

Check this out๐Ÿ‘‡
github.com/axolotl-ai-cloโ€ฆ
Read 6 tweets

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