I wrote a lot of web scraping libraries back in the day in C#. I had considered porting my library to Python.
Python Simplified discusses the benefits of using Mechanical Soup over Beautiful Soup for web scraping.
In this video, Jin-Sung Kim, PhD explains how some quantum algorithms can outperform their classical counterpart and shows us how to implement Grover’s Algorithm.
In this episode, Seth and Tania will talk about the Python community and the scientific Python ecosystem. So if you always wanted to know what is so great about Python for Machine learning and its community this episode is for you.
Vicky Harp joins Scott Hanselman to show how Azure Data Studio combined the simple and robust SQL query editing experience of tools like SSMS with the flexibility and collaboration of Jupyter Notebooks. The November 2019 release of Azure Data Studio included SQL Server 2019 Guide as a Jupyter Book, which provides a richer troubleshooting experience.
Jeffrey Mew shows you how you can can natively edit Jupyter notebooks in Visual Studio Code.
Jupyter (formerly IPython) is an open-source project that enables you to easily combine Markdown text and executable Python source code on one canvas called a notebook.
These notebooks contain live code, equations, visualizations and narrative text. Jeffrey shows how easy it is to work with Jupyter notebooks in Visual Studio Code.
Here’s a great collection of Jupyter notebooks that explore all the new features of SQL Server 2019.
Here are some of the ones that caught my attention.
Ever wondered what breed that dog or cat is? In this show, you’ll learn how to train, optimize and deploy a deep learning model using Azure Notebooks, Azure Machine Learning Service, and Visual Studio Code using Python. Using transfer learning to retrain a mobilenet model via Tensorflow to recognize dog and cat breeds using the Oxford IIIT Pet Dataset.
Next, watch how to optimize that model using the Azure Machine Learning Service HyperDrive service, and improve the accuracy of our model to over 90%. Finally, we’ll put on our developer hat, and use Visual Studio Code and our Python Extension to deploy and test our model. Along the way you’ll see cool features like our new Jupyter-powered interactive programming experience in VS Code, our AI powered IntelliSense feature called Intellicode, and our Azure Machine Learning extension.
HyperDrive service, and improve the accuracy of our model to over 90%. Finally, we’ll put on our developer hat, and use Visual Studio Code and our Python Extension to deploy and test our model. Along the way you’ll see cool features like our new Jupyter-powered interactive programming experience in VS Code, our AI powered IntelliSense feature called Intellicode, and our Azure Machine Learning extension.
Github repo for all code used in the show: https://github.com/microsoft/connect-petdetector
Blog post introducing the new features in Azure Notebooks: https://github.com/Microsoft/AzureNotebooks/wiki/Azure-Notebooks-at-Microsoft-Connect()-2018
Blog post introducing our data science features in our Python extension: https://blogs.msdn.microsoft.com/pythonengineering/2018/11/08/data-science-with-python-in-visual-studio-code/
Azure Notebooks: https://notebooks.azure.com
Python Extension: https://marketplace.visualstudio.com/items?itemName=ms-python.python
Azure Machine Learning Extension: https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai
Visual Studio Code: https://code.visualstudio.com/
Here’s an interesting talk from PyCon Germany by Joshua Görner, a Data Scientist at BMW.
From the video description:
Interactive notebooks like Jupyter have become more and more popular in the recent past and build the core of many data scientist’s workplace. Being accessed via web browser they allow scientists to easily structure their work by combining code and documentation. Yet notebooks often lead to isolated and disposable analysis artifacts. Keeping the computation inside those notebooks does not allow for convenient concurrent model training, model exposure or scheduled model retraining. Those issues can be addressed by taking advantage of recent developments in the discipline of software engineering. Over the past years containerization became the technology of choice for crafting and deploying applications. Building a data science platform that allows for easy access (via notebooks), flexibility and reproducibility (via containerization) combines the best of both worlds and addresses Data Scientist’s hidden needs.