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:

I have been coding in Python for 8 years now. ⏳

If I were to start over today, here's a roadmap...👇 1️⃣ freeCodeCamp

4 hours Python bootcamp!!

What you'll learn:
- Installing Python
- Setting up an IDE
- Basics Syntax
- Variables & Datatypes
- Looping in Python
- Exception handling
- Modules & pip
- Mini hands-on projects 🔥

Check this out 👇

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 👇

Self-attention in transformers clearly explained: Before we start a quick primer on tokenization!

Raw text → Tokenization → Embedding → Model

Embedding is a meaningful representation of each token (roughly a word) using a bunch of numbers.

This embedding is what we provide as an input to our language models.

Check this👇

Let's master Python's pathlib module today: Pathlib is a Python module that turns file system paths into easy-to-manage objects, making your code cleaner and more intuitive.

Today, I'll explain everything you need to know about pathlib!

Let's go! 🚀

I started my career in Data Science back in 2016 ⏳

Being self-taught, YouTube has been a great source of learning for me!

Here are some of the channels I've ardently followed: 1️⃣ StatQuest:

BAM!!

Joshua Stammer's fun take on teaching Stats & Mathematics for ML to masses is an absolute gold mine 🥇

Check this out 👇
youtube.com/c/joshstarmer

Let's compare Llama-3 & Phi-3 using RAG: Before we begin, take a look at what we're about to create!

As always, I'll be using my favourite stack:

- @Ollama for locally serving a LLM (Llama-3)
- @Llama_Index for orchestration
- @Streamlit for building the UI
- @LightningAI for development & hosting

Let's go! 🚀

Backpropagation in PyTorch, clearly explained: Backpropagation is the key to optimizing our neural network by adjusting it's weights.

And it's done by calculating gradients of the loss function w.r.t. these weights!

Today, we learn how to do this using PyTorch...👇

Cosine similarity clearly explained (a go-to metric for vector similarity): A geometric intuition behind the most commonly used similarity metric in modern Vector Databases!

Let's go! 🚀

Let's build a RAG app using MetaAI's Llama-3 (100% local): Before we begin, take a look at what we're about to create!

Here's what you'll learn:

- @Ollama for locally serving a LLM (Llama-3)
- @Llama_Index for orchestration
- @Streamlit for building the UI
- @LightningAI for development & hosting

Let's go! 🚀

SQL flavoured JOINS in pandas, clearly explained: Let's setup 2 DataFrames to perform merge operations.

• left
• right

A, B, C are the common keys ✅

Check this 👇

Let's implement & train this neural network step-by-step, from scratch using PyTorch!

1/n First of all let's define our model in PyTorch:

2/n

Autoencoders clearly explained: Autoencoders are one of my favourite Neural Networks!

They are simple yet so powerful!

Today, We'll understand:

- What Autoencoders are❓
- How they are trained
- The applications of Autoencoders
- And how to implement them from scratch using PyTorch Lightning!⚡️

Let's go! 🚀

LEGB rule in Python, clearly explained: Every Python developer must know the LEGB rule!

It's crucial for avoiding major bugs!

And today, I will provide a clear explanation of the same!

Let's go! 🚀

Let's build a "Chat with your docs" RAG application, step-by-step: Before we begin, take a look at what we're about to create!

We'll be using:

- @Cohere highly capable ⌘R+ as the LLM
- @Llama_Index for orchestration
- @Streamlit for the UI

Everything in just ~170 lines of Python code, that I've shared at the end! 🔥

Let's go! 🚀

Matplotlib subplots & subplot-mosaic clearly explained: Today, we learn about:

- Regular subplots
- Subplot-mosaic 🔥

Let's go! 🚀

5 GitHub repositories that will give you superpowers as an AI/ML Engineer: 1️⃣ Awesome Artificial Intelligence

A curated list of Artificial Intelligence:

- courses
- books
- video lectures
- and papers with code

Check this out 👇
github.com/owainlewis/awe…

This is the future of building RAGs. Let me introduce you to DSPy today: In terms of analogy, DSPy is to RAG as PyTorch is to DNNs.

DSPy : RAG :: PyTorch : DNNs

To understand DSPy, we need to grasp three key concepts:

- Signatures
- Modules
- Optimizers

Let's delve into each one! 🚀

Tensors in PyTorch, clearly explained: Tensors are the fundamental building blocks for performing mathematical operations in deep learning models.

Today, I will provide a comprehensive explanation with illustrative code examples.

Let's go! 🚀

7 projects that every AI engineer must explore: 1️⃣ Chat with your code using RAG

A step by step guide to building a RAG application using @llama_index!

Check this out👇
lightning.ai/lightning-ai/s…

Eigenvalues & Eigenvectors clearly explained: The concept of eigenvalues & eigenvectors is widely known yet poorly understood!

Today, I'll clearly explain their meaning & significance.

Let's go! 🚀