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...👇

Let's build a RAG app using MetaAI's Llama-3.1 (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-3.1
- @LightningAI for development & hosting

Let's go! 🚀

SQL Joins clearly explained: Let's setup 2 DataFrames to perform merge operations & name them:

• left
• right

A, B, C are the common keys ✅

Check this 👇

Illustrated Guide to Tensor Parallelism (supercharge your LLM training): What is Tensor Parallelism?

Tensor Parallelism is form of model parallelism. It splits individual tensors across GPUs for efficient computation and memory use.

Perfect for training LLMs!

Let's simplify it today! 🚀

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

Let's build an advanced "Chat with your code" RAG application (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 vectorDB
- LlamaIndex's advanced code parsers
- @Ollama for serving LLMs locally

Let's go! 🚀

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

Multiprocessing might be the solution you're looking for!

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

Let's go! 🚀

Autoencoders are one of my favourite neural networks!

Today, I'll clearly explain:

- What they are❓
- And how they work❓

Let's go! 🚀

1/n Autoencoders have two main parts:

1️⃣ Encoder: Compresses the input into a dense representation (latent space)

2️⃣ Decoder: Reconstructs the input from this dense representation.

The idea is to make the reconstructed output as close to the original input as possible:👇

2/n

ML models can only be as good as the data they're trained on!

Introducing Datalab, now you can automatically detect:

- outliers
- label errors
- (near) duplicates
- low-quality/non-IID sampling

Developed at MIT, Datalab works with all types of data & any trained model!

1/n How to use Datalab❓

Datalab works with any ML model you have already trained!

It's like a magic wand! 🪄

Inspecting your dataset with Datalab merely requires the code below! 👇

2/n

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! 🚀

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! 🚀

Let's make RAG 40x faster and 32x memory efficient: To achieve this we'll be leveraging binary quantization! 🚀

And here's what you'll learn, today:

- Intuitive explanation of Binary Quantization (BQ)
- Self-host @qdrant_engine with BQ enabled
- Search over 36M+ vectors in <50ms 🔥

Let's go! 🚀

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! 🚀

f-strings in Python clearly explained: f-strings were introduced in Python 3.6 and have since become a favorite among developers for their simplicity and readability.

Today, we'll start with the basics and dive into all the ninja tricks of using f-strings.

Let's go! 🚀

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! 🚀

Object oriented programming in Python, clearly explained: We break it down to 6 important concepts:

- Object 🚘
- Class 🏗️
- Inheritance 🧬
- Encapsulation 🔐
- Abstraction 🎭
- Polymorphism 🌀

Let's take them one-by-one... 🚀

I started my career in Data Science back in 2016 ⏳

Here's a detailed roadmap for those starting out today!

What's covered:
- Python
- Machine Learning
- Maths for ML
- ML Books
- MLOps
- LLMs/AI Engineering

Read more...👇 1️⃣ Python

If you are new to programming and just getting started.

There isn't a better place to learn Python than David J Malan's CS50p.

Beautiful explanations and great projects.
It's a complete package ⚡️

Check this out 👇
edx.org/course/cs50s-i…

Let's learn how to evaluate a RAG application: Here's what we'll do today:

- Build a RAG pipeline using @llama_index
- Evaluate it with @ragas_io
- Implement observability using @ArizePhoenix

Before we dive in, check out this demo: