Poetry for Julia + Deep Learning!

Week 32-2023 [Issue 3.0]

Welcome to The Repo! 🚀
Each week, I'll curate three gems from the data science community:

🗄️ Re: Remarkable Repository

💻 P: Prolific Programmer

🏢 O: Outstanding Organization

I hope you find them as valuable and insightful as I do.
You can find all recommendations in the GitHub repository at finnoh/repo!

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python-poetry/poetry

🗄️ Repository | Python framework to manage your project's environment

Spreading the idea of Poetry is crucial because it encourages teamwork and the sharing of code, which are both vital for open-source development.
Have you ever faced problems with Python coding in different environments? For instance, your friend might use a different operating system, Python version, or even a new module in their code, which ends up breaking the entire project. I've been in that situation, and so have many others. This is where Poetry comes in. It stores all the dependencies needed for your project right in your repository. When all collaborators use Poetry, everyone works within the same coding environment. This ensures that the code functions properly for everyone, or at the very least, won't fail due to varying package versions. Make sure you also visit Poetry's documentation, as this framework enables you to do a lot more than just maintain a coherent programming environment. Poetry also enables you to turn your project, easily, into an installable Python package.

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Patrick Altmeyer

💻 Programmer | Writing on Julia, Quarto, and AI by a Ph.D. Student

Patrick's blog covers exciting topics in data science and programming, with an emphasis on Julia, a programming language I want to get into. Besides Julia, he has posted about Quarto, which is also the framework behind his blog, and a topic I have written about. If I had to pick two great first articles to read, I would recommend Quarto on Steroids: Advanced Customization through Quarto Extensions and Building a Conformal Chatbot in Julia.

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Deep Learning, by Goodfellow, Bengio, and Courville - 2016

🏢 Organization | A classic on the math and theory behind deep learning

This week, we finish with a classic: The book Deep Learning, by Goodfellow, Bengio, and Courville (2016-MIT Press). While you can buy the book on Amazon, there is also this free online version for students and practitioners. The book will take you from the fundamentals of Linear Algebra and Probability theory to an understanding of Deep Learning, which enables you to understand the most recent advances in the field and is hence both a great teacher and a great resource to consult.

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