As a programmer, I often ask myself: what am I doing?!
Python, as a one-stop-shop for Machine Learning, Geospatial Analytics, Optimization Algorithms, and Visualization Tools, can be leveraged to create a simple yet effective sustainability decision-making methodology with just four steps: Geospatial Indexing, Feature Engineering, Predictive Scoring, and Score Optimization.
With it, it's possible to tackle distinct sustainable development issues, from mobility transition to light pollution in a way that decision-makers can quickly implement it to take action for the Sustainable Development Goals.
Working with data can be challenging: it often doesn’t come in the best format for analysis, and understanding it well enough to extract insights requires both time and the skills to filter, aggregate, reshape, and visualize it. This session will equip you with the knowledge you need to effectively use the pandas library to make this process easier.
The secret of becoming a FastAPI Expert will be revealed. 🤫
On this talk, you'll understand how you can help the community, and receive guidelines
to be become an expert yourself. 🔥
In this session, we will train a ML model to predict future ROI of variable advertising spend budgets across multiple channels including search, video, social media, and email using Snowpark for Python and scikit-learn.
Medical Charts contain information which is documented in traditional Handwritten format which cannot be processed by NLP. Let's dig into how to build an API which detect handwritten information using FastAPI.
Do you like sending 200 emails, checking for appointments on a website every 15 minutes or copying a lot of files? No? Thought so! That’s why in this talk you will learn how to automate all of these tasks and more using the magical powers of Python.
Property-based testing is a great benefit to the robustness and maintainability of your software. Yet, the technique is still vastly underused in the Python community. The workshop gives a hands-on introduction to Hypothesis and practices different approaches for writing property-based tests.
Apps such as Obsidian.md have revolutionised note-taking for the digital age, through connected markdown files. I discuss how I developed a Python package that enabled me to become more effective at learning at university and built a knowledge graph of 500+ notes.
Natural Language Processing (NLP) is one of the most exciting fields in Machine Learning and AI. In this beginner-friendly workshop, I will introduce Keras as a Python library for applying NLP techniques on a multi-label emotion classification dataset and present how to use it to build a deep learning classifier. Besides, I will also compare and demonstrate how applying NLP techniques improve the predictability of a model and then, tune a neural network using Keras Tuner to boost the overall model performance.
Have you ever looked at a text and wondered how on earth it could be used in a machine learning model? How do we get models to understand what we’re reading? In this talk, we’ll examine different ways we can extract meaning from text for use in modelling.
This year, I took over two open-source Python projects that hadn’t been maintained for years. They used deprecated frameworks and old Python versions. Despite the projects being heavily io-driven and calling web-based APIs, they used synchronous methods. A refactor had been started, then never finished. There were branches full of uncommitted code, tests that tested nothing, irrelevant dependencies… you name it!
After making progress with getting the code into shape, I distilled what I learned into clear steps. When you leave this talk, you will know exactly what to do when you want to improve the performance, security and development ease of Python projects you maintain.
We’ll talk about how to fix all of the above and more, including: moving to async programming, adding type validation with Pydantic, overhauling tests, updating without anything exploding, and choosing the right dependencies.
Finally, I’ll give you a take-home checklist for updating an older project so doing this at home can be efficient and pain-free.
Recommendation algorithms are the driving force of many businesses: e-commerce, personalized advertisement, on-demand entertainment. Computer algorithms know what you like and present you with things that are customized for you. Here we will explore how to do that by building a system ourselves.
What happens on the computer when you run print(“Hello world”)? This talk attempts to dissect how Python code gets translated for execution. While many programmers can live without interacting with compiler internals, a stronger understanding of CPython can help make us better programmers.
Sunset Party sponsored by Issuu