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Akshay 🚀 Profile picture
@akshay_pachaar
Mar 20 • 8 tweets • 4 min read • Read on X
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Machine Learning cheatsheets form Stanford's CS 229:
1️⃣ Supervised Machine Learning

Covers:

- Notations & general concepts
- Linear regression
- Generalised linear models
- Gaussian discriminant analysis
- Tree based & ensemble methods

🔗 tinyurl.com/CS229-Supervis…
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2️⃣ Unsupervised Machine Learning

Covers:

- PCA
- K-means clustering
- Hierarchical clustering
- Expectation maximization
- Clustering evaluation metrics

🔗 tinyurl.com/CS229Unsupervi…
Image
3️⃣ Deep Learning

Covers:

- Neural networks
- Convolutional neural networks
- Recurrent neural networks
- Reinforcement learning & control

🔗 tinyurl.com/CS229DeepLearn…
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4️⃣ ML Tips & Tricks

Covers:

- Model selection
- Model Evaluation
- Diagnostics (Bias/Variance)

🔗 tinyurl.com/CS229MLTipsTri…
Image
5️⃣ Probability & Statistics

Covers:

- Random Variables
- Probability & combinatorics
- Conditional probability
- Parameter estimation

🔗 tinyurl.com/CS229ProbStats
Image
6️⃣ Algebra & Calculus

Covers:

- General notations
- Matrix operations
- Matrix properties
- Matrix calculus

🔗 tinyurl.com/CS229LinalgCal…
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If you interested in:

- Python 🐍
- ML/MLOps 🛠
- CV/NLP 🗣
- LLMs/AI Engineering ⚙️

Find me → @akshay_pachaar ✔️

I also write a FREE weekly Newsletter @ML_Spring on AI Engineering!
Join 7k+ readers: mlspring.beehiiv.com
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More from @akshay_pachaar

Akshay 🚀 Profile picture
Mar 15
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... 🚀 Image
1️⃣ Object 🚘

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

...👇
Read 11 tweets
Akshay 🚀 Profile picture
Mar 12
A strong foundation in Mathematics can help you excel in the field of Data Science!

Here are some of the top FREE resources on Maths for ML, covering:

- Linear Algebra
- Calculus
- Prob/Stats
- Applied Bayesian Modeling
- Probabilistic Machine Learning

Let's go! 🚀 Image
1️⃣ Linear Algebra: Gilbert Strang

Arguably, the best linear algebra course out there, taught by MIT's legendary Professor Gilbert Strang.

Check this 👇
youtube.com/playlist?list=…
Image
2️⃣ Essence of Linear Algebra: 3Blue1Brown

A free course offering the core concept of linear algebra with a visuals-first approach.

Check this out👇
youtube.com/playlist?list=…
Image
Read 9 tweets
Akshay 🚀 Profile picture
Mar 8
Self-attention as a directed graph!

Self-attention is at the heart of transformers, the architecture that led to the LLM revolution that we see today.

In this post, I'll clearly explain self-attention & how it can be thought of as a directed graph.

Read more...👇 Image
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👇 Image
The core idea of Language modelling is to understand the structure and patterns within language.

By modeling the relationships between words (tokens) in a sentence, we can capture the context and meaning of the text. Image
Read 9 tweets
Akshay 🚀 Profile picture
Mar 7
Let's build a "Chat with your code" RAG application, step-by-step:
Before we begin, take a look at what we're about to create!

We'll be using:

- LlamaIndex for orchestration
- Ollama for serving LLMs locally
- Streamlit for the UI

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

Let's go! 🚀
The architecture diagram presented below illustrates some of the key components & how they interact with each other!

It will be followed by detailed descriptions & code for each component: Image
Read 11 tweets
Akshay 🚀 Profile picture
Mar 5
PDF and PMF clearly explained: Image
Before we start, we must understand a few keywords:

- Random variable
- Discrete random variable
- Continuous random variable

Let's take them one-by-one ...👇
Random Variable:

A variable that represents the outcome of a trial/experiment or an event.

Think of it as a function that maps the outcomes of a random process to a set of real numbers.

Usually represent by: X

Check this out 👇 Image
Read 10 tweets
Akshay 🚀 Profile picture
Mar 4
Key concepts to understand if you're working with LLMs:
1️⃣ The Transformer

Transformers brought the AI revolution we see today, with their ability to process data in parallel & the attention mechanism.

Here's an illustrated guide to understanding self-attention in transformers:
mlspring.beehiiv.com/p/attention-ne…
2️⃣ Prompt Engineering

The art of crafting inputs (prompts) that guide LLMs to generate desired outputs. Think of it as communicating effectively with AI.

Here's a complete guide to prompt engineering:
mlspring.beehiiv.com/p/guide-prompt…
Read 8 tweets

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