15 DS/ML Cheat Sheets

I have published several cheat sheets (or single-frame summary sheets) in this newsletter before.

Today, I am collating them into a single newsletter issue and sharing with you.

Since the cheat sheets are pretty self-explanatory, I won’t provide much details.

Yet, a link to the corresponding issue has been provided for further reference.

Enjoy!

#1) 15 Pandas ↔ Polars ↔ SQL ↔ PySpark Translations

#2) 4 Strategies for Multi-GPU Training

Who likes to stare at a screen during model training? Single-GPU training is rarely desirable if you want to train large models. These four strategies help:

Implementation is available here

• A Beginner-friendly Guide to Multi-GPU Model Training

• A Practical Guide to Scaling ML Model Training.

#3) 4 Ways to Test ML Models in Production

Instantly replacing an old model with a new one can be a terrible idea. Yet, these are risk-free ways to test the model before replacing the old model:

We covered their implementation here: 5 Must-Know Ways to Test ML Models in Production (Implementation Included).

#4) 15 Ways to Optimize Neural Network Training

Learn what MLEs are actually supposed to do—engineer models, not just train them.

We covered the implementations here: 15 Ways to Optimize Neural Network Training (With Implementation).

#5) Training and Inference Time Complexity of 10 ML Algorithms

Understand whether a particular algorithm is good to proceed with based on your dataset.

#6) 5 LLM Fine-tuning Techniques Explained Visually

Given the scale, traditional fine-tuning does not help in LLMs. These are 5 common strategies:

We covered them in detail here:

• Understanding LoRA-derived Techniques for Optimal LLM Fine-tuning.

• Implementing LoRA From Scratch for Fine-tuning LLMs.

#7) NumPy Cheat Sheet Of 40 Most Used Methods

Having used NumPy for years, I can confidently say that you will use these methods 95% of the time working with NumPy.

#8) Most Important Plots in Data Science

#9) Must-know Data Science Glossary

Some of the most common terms that data scientists must know.

#10) 10 Regression and Classification Loss Functions

#11) 11 Types of Variables in a Dataset

#12) 7 Categorical Data Encoding Techniques

#13) 20 Most Common Magic Methods

#14) Full-model Fine-tuning vs. LoRA vs. RAG

#15) Transfer Learning, Fine-tuning, Multitask Learning and Federated Learning

We covered Federated learning in detail with implementation here: Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning.

That’s a wrap!

👉 Over to you: What more cheat sheets (or single-frame summaries) would you like me to share next?

For those who want to build a career in DS/ML on core expertise, not trends:

Every week, I publish no-fluff deep dives on topics that truly matter to your skills for ML/DS roles.

For instance:

• 15 Ways to Optimize Neural Network Training (With Implementation)

• A Mathematical Deep Dive Into the Curse of Dimensionality

• A Crash Course on Graph Neural Networks – Part 1

• A Crash Course on Graph Neural Networks – Part 2

• A Crash Course on Graph Neural Networks – Part 3

• Formulating and Implementing XGBoost From Scratch

• A Crash Course of Model Calibration – Part 1

• A Crash Course of Model Calibration – Part 2

• Conformal Predictions: Build Confidence in Your ML Model’s Predictions

• Quantization: Optimize ML Models to Run Them on Tiny Hardware

• A Crash Course on Causality – Part 1

• A Crash Course on Causality – Part 2

• 5 Must-Know Ways to Test ML Models in Production (Implementation Included)

• A Beginner-Friendly Guide to Multi-GPU Model Training

• Implementing Parallelized CUDA Programs From Scratch Using CUDA Programming

• And many many more.

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