College Precalculus – Full Course with Python Code by Ed Pratowski and freeCodeCamp focus on the foundation of calculus with Python implementation. This 12 hours course covers the following topics:
✅ Core trigonometry
✅ Matrix operation
✅ Working with complex numbers
✅ Probability
FreeCodeCamp released today a new course for fine tuning LLM models. The course, by Krish Naik, focuses on different tuning methods such as QLORA, LORA, and Quantization using different models such as Llama2, Gradient, and Google Gemma model.
Here is a great resource for getting started with Observable Framework by Allison Horst. Observable Framework is an open-source JS library for creating dashboards. The sequence of videos covers how to set up a project and data loader, customize the dashboard, and deploy it.
I've been using Quarto for over a year now and I fully endorse it for technical writing, a game changer that can help get your work out there 🚀 If you're new to this space and want to get involved I highly recommend this as a starting point: https://youtu.be/_f3latmOhew?si=VxcMut5INSgK4pyP#DataScience
The End To End Data Science With R is a new book by Rene Essomba. The book, as the name implies, focuses on the core data science applications using R ❤️. This book covers the following topics:
✅ Exploratory data analysis
✅ Data visualization
✅ Supervised learning
✅ Unsupervised learning
✅ Time series
✅ Natural language processing
✅ Image classification
I'm looking for consulting work in data science/data analysis. I'm based in Christchurch, New Zealand, but I'm happy to work remote.
I have a background in media & communication, so I'm happy working with clients of any technical ability ^_^ I've worked on projects ranging from surveys of social workers to network analysis, so I'm happy to try anything!
The MLX is Apple's framework for machine learning applications on Apple silicon. The MLX examples repository provides a set of examples for using the MLX framework. This includes examples of:
✅ Text models such as transformer, Llama, Mistral, and Phi-2 models
✅ Image models such as Stable Diffusion
✅ Audio and speech recognition with OpenAI's Whisper
✅ Support for some Hugging Face models
(1/2) Hands-On Mathematical Optimization with Python 🚀
The Hands-On Mathematical Optimization with Python book by Krzysztof Postek, Alessandro Zocca, Joaquim Gromicho, and Jeffrey Kantor provides the foundation for mathematical optimization. As the name implies, the book is hands-on with Python examples, mainly using Pyomo.
Data Wrangler is a new Microsoft VScode extension for data exploratory analysis. It supports Python 🐍 and Pandas 🐼 DataFrame objects and is integrated into VScode Jupyter Notebooks. Here are some of the functionalities of Data Wrangler:
✅ Data review
✅ Column filtering
✅ Summary statistics
✅ Data cleaning and transformation
✅ Hadeling missing values
✅ Creating new fields
Last December we published a paper in the "Datasets and Benchmarks" track at NeurIPS 2023, detailing some of our ideas of how @renku could used for a more sustainable practice around data sets in data science, machine learning and beyond. It was quite well received, earning a "spotlight" acceptance! 🎉 More details here: https://blog.renkulab.io/neurips-2023
MIT launched the 2024 edition of the Introduction to Deep Learning course by Prof. Alexander Amini and Prof.Ava Amini. The course started at the end of April and will run until June. The course lectures are published weekly. The course syllabus keeps changing from year to year, reflecting the rapid changes in this field.
(1/2) I recently posted a few posts about Rust 🦀 and my intention to leverage it for data science applications. Multiple people asked if Rust is a substitute for R or Python, and the short answer (in my opinion) is no. I see Rust as a complementary or supporting language that could make languages like R and Python faster.
Polaris 🐻❄️ is one example of a Python 🐍 application that uses Rust on the backend. 🧵👇🏼