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Akshay πŸš€ Profile picture
@akshay_pachaar
Jul 1, 2022 β€’ 7 tweets β€’ 3 min read β€’ Read on X
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πŸ”΄ NumPy Fundamentals: Indexing & Slicing
🟑 Jupyter Notebook πŸ“’ Available βœ…

A thread 🧡 πŸ‘‡

#DataScience #MachineLearning #100daysOfCode
πŸ’« Basic Indexing ⬇️
πŸ’« Slicing and Striding ⬇️
πŸ’« Integer array Indexing ⬇️
πŸ’« Boolean array Indexing ⬇️
πŸ”΅ Find Jupyter Notebook πŸ“’ ⬇️
github.com/patchy631/twit…
Hope you enjoyed reading!! πŸ“–

You can follow me if you are interested in:
βœ… Python 🐍
βœ… Machine learning πŸ€–
βœ… Computer Vision πŸŽ₯
βœ… MlOps βš’οΈ

Retweet πŸ” the first tweet πŸ₯ .
Cheers!! 🍻

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More from @akshay_pachaar

Akshay πŸš€ Profile picture
May 18
List comprehension in Python clearly explained:
Simply put, list comprehension are a concise way to replace multi-line for loops with a single line of code!

And, they are reasonably faster πŸ”₯

Let's take a quick look at the Syntax before we break it down with examples!πŸ‘‡ Image
We start with a simple example!

Here we want to create a list of the squares of numbers from 0 to 4.

Check this out πŸ‘‡ Image
Read 8 tweets
Akshay πŸš€ Profile picture
May 17
Retrieval Augmented Generation (RAGs), clearly explained:
Imagine creating a ChatGPT-like interface that taps into our own knowledge base to answer our queries.

That's precisely what RAG offers you! ✨

Today, I'll delve into each component required to develop a RAG application and share a working project by the end!

Let's go! πŸš€
1️⃣ Custom knowledge base:

Custom Knowledge Base: A collection of relevant and up-to-date information that serves as a foundation for RAG.

It can be a database, a set of documents, or a combination of both. Image
Read 10 tweets
Akshay πŸš€ Profile picture
May 16
K-Nearest Neighbours (KNN), clearly explained:
KNN involves 4 simple steps:

1️⃣ Selecting the the value of K

2️⃣ Calculate the distance between the new & existing data points

3️⃣ Find K closest points (neighbours)

4️⃣ For classification, vote for the majority class, for regression, compute the average.

Let's code it ...πŸ‘‡ Image
Here's a didactic implementation of KNN from scratch!

Seeing things in code makes our understanding more concrete! πŸ‘Š

Check this out πŸ‘‡ Image
Read 7 tweets
Akshay πŸš€ Profile picture
May 15
Let's learn how to evaluate a RAG application (part 1):

1/n
To evaluate a typical RAG application, we need two things:

- A set of questions
- And ground truth answers for these questions

Let's see how to do it automatically using ragas in this step-by-step guide that follows.

2/n Image
1️⃣ Loading Knowledge Base

We load and chunk the raw data from which we will create our evaluation dataset:

3/n Image
Read 8 tweets
Akshay πŸš€ Profile picture
May 10
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! πŸͺ„βœ¨

Check this outπŸ‘‡
github.com/cleanlab/clean…
2️⃣ LazyPredict

A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code.

Supports both regression & classification! ✨

Check this outπŸ‘‡
pypi.org/project/lazypr…
Image
Read 12 tweets
Akshay πŸš€ Profile picture
May 8
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 πŸ‘‡ Image
So what is Conditional probability ⁉️

It's a measure of the probability of an event given that another event has occurred.

If the events are A and B, we denote this as P(A|B).

This reads as "probability of A given B"

Check this illustration πŸ‘‡ Image
Read 10 tweets

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