Pythonasync, await, generators, tasks, coroutines, oh my! Let's learn how this often overlooked module can help you write concurrent code, without starting multiple threads or using multiple CPU cores. You'll leave with a better understanding of what cooperative multitasking is and how you can use it in your own projects.
PythonIn the 13 years since Python 3 started diverging from Python 2, Python 3's gained many new features: from co-operative async programming, to optional static typing, and to string templating. These solve performance issues, maintainability issues, and developer productivity.
In 40 minutes, we'll show you the best new features from the last few releases of Python, why they're in Python, and how to use them. We'll have example code!
PythonScikit learn, decision forests, pandas, numpy, Jupyter Notebooks, where do I start? Machine learning can help you analyze data and get information to make better decisions, but it can also be intimidating. In this session I'll explain the basic steps required to train a machine learning model then we'll see how that looks in actual code using Python code in a Jupyter Notebook.
PythonObject Detection has become very popular during the last few years. It may look complex from afar,How to train a model on my own dataset ? How do I label data? Which architecture should I choose ? What are the standard metrics to evaluate my model ? How to deploy my model ?
In this talk, we will go over a concrete example to present the typical journey to deploy an object detection application in python and answers those questions and many more.
PythonChoosing the right evaluation metric for your machine learning project is crucial, as it decides which model you’ll ultimately use. How do you choose an appropriate metric? This talk will explore the important evaluation metrics used in regression and classification tasks, their pros and cons, and how to make a smart decision.
PythonAre you trying to learn data science? Do you know SQL? Pandas is an open source library for loading, querying, and cleaning up data for machine learning. In this session I'll translate SQL into Pandas. What's the equivalent of a table? Can you create an index? How do I write a SELECT * FROM WHERE with Pandas. Find out why SQL lovers fall in love with Pandas for data science
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