Here’s an interesting video posted to the Microsoft Research YouTube channel about graph neural networks. Graph structured data types are a natural representation for such systems, and several architectures have been proposed for applying deep learning methods to these structured objects.
3Blue1Brown has a great video exploring neural networks and the important role that gradient descent plays in machine learning.
I’ve long thought that the technology sometimes resembled the fashion industry in that trends come, go, and come back albeit in slightly differently. The recent rise of “edge computing” bears witness to this idea.
In this video, a16z partner Peter Levine takes us on a “crazy” tour of the history and future of cloud computing — from the constant turns between centralized to distributed computing, and even to his “Forrest Gump rule” of investing in these shifts.
In case you haven’t noticed, Data Science has become more and more popular as a career choice as it offers both lucrative salaries and the opportunity to have a huge impact.
The Data Science interview process is challenging, but with dedicated practice you can succeed. In this video, Siraj Raval outlines the 7 steps to pass any Data Science Interview.
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/
In this talk from the most recent O’Reilly AI Conference, Laurence Moroney from Google talked about Machine Learning, AI, Deep Learning and more, and how they fit the programmers toolkit. He introduced what it’s all about, cutting through the hype, to show the opportunities that are available in Machine Learning. He also introduced TensorFlow, and how it’s a framework that’s designed to make Machine Learning easy and accessible, and how intelligent apps that use ML can run in a variety of places including mobile, web and IoT.