Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.
1οΈβ£4 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
2οΈβ£4 ways to test models in production
- While testing a model in production might sound risky, ML teams do it all the time, and it isnβt that complicated. - Implemented here: https://lnkd.in/g33mASMM
3οΈβ£Training & 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
4οΈβ£Regression & 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
5οΈβ£Transfer 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
6οΈβ£15 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
7οΈβ£11 most important plots in data science
- A must-have visual guide to interpret and communicate your data effectively. - Explained here: https://lnkd.in/geMt98tF
8οΈβ£11 types of variables in a dataset
Understand and categorize dataset variables for better feature engineering. - Explained here: https://lnkd.in/gQxMhb_p
9οΈβ£NumPy 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 walkthroughβfrom 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.
1οΈβ£First of all, strengthen your foundation (math and statistics) .
βοΈ If you don't know math, you'll run into trouble wherever you go. Every model you build, every analysis you do, there's a world of math behind it. You need to know these things well:
π¨π»βπ» Real learning means implementing ideas and building prototypes. It's time to skip the repetitive training and get straight to real data science projects!
π With the DataSimple.education website, you can access 40+ data science projects with Python completely free ! From data analysis and machine learning to deep learning and AI.
βοΈ There are no beginner projects here; you work with real datasets. Each project is well thought out and guides you step by step. For example, you can build a stock forecasting model, analyze customer behavior, or even study the impact of major global events on your data.
βπ³οΈβπ40+ Python Data Science Projects βπWebsite
