👨🏻💻 For the past few days, I've been busy preparing this comprehensive tutorial on the NumPy library for data science, trying to cover all the tips and tricks of this library.
✅Why is this booklet different? Because it is not written based on just theoretical concepts, but is the result of my own experiences and learning. It has real and practical examples that will help you better understand #NumPy concepts and use them in your projects.💯
Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.
1️⃣4 strategies for Multi-GPU Training.
- Training at scale? Learn these strategies to maximize efficiency and minimize model training time. - Read here: https://lnkd.in/gmXF_PgZ
2️⃣4 ways to test models in production
- While testing a model in production might sound risky, ML teams do it all the time, and it isn’t that complicated. - Implemented here: https://lnkd.in/g33mASMM
3️⃣Training & inference time complexity of 10 ML algorithms
Understanding the run time of ML algorithms is important because it helps you: - Build a core understanding of an algorithm. - Understand the data-specific conditions to use the algorithm - Read here: https://lnkd.in/gKJwJ__m
4️⃣Regression & Classification Loss Functions.
- Get a quick overview of the most important loss functions and when to use them. - Read here: https://lnkd.in/gzFPBh-H
5️⃣Transfer Learning, Fine-tuning, Multitask Learning, and Federated Learning.
- The holy grail of advanced learning paradigms, explained visually. - Learn about them here: https://lnkd.in/g2hm8TMT
6️⃣15 Pandas to Polars to SQL to PySpark Translations.
- The visual will help you build familiarity with four popular frameworks for data analysis and processing. - Read here: https://lnkd.in/gP-cqjND
7️⃣11 most important plots in data science
- A must-have visual guide to interpret and communicate your data effectively. - Explained here: https://lnkd.in/geMt98tF
8️⃣11 types of variables in a dataset
Understand and categorize dataset variables for better feature engineering. - Explained here: https://lnkd.in/gQxMhb_p
9️⃣NumPy cheat sheet for data scientists
- The ultimate cheat sheet for fast, efficient numerical computing in Python. - Read here: https://lnkd.in/gbF7cJJE
NumPy is an essential library in the world of data science, widely recognized for its efficiency in numerical computations and data manipulation. This powerful tool simplifies complex operations with arrays, offering a faster and cleaner alternative to traditional Python lists and loops.
The "Mastering NumPy" booklet provides a comprehensive walkthrough—from array creation and indexing to mathematical/statistical operations and advanced topics like reshaping and stacking. All concepts are illustrated with clear, beginner-friendly examples, making it ideal for anyone aiming to boost their data handling skills.
