Registries are used in every industry and in multiple scenarios. Blockchain-based registries that are shared, immutable and cryptographically secure serve an important need, but it’s not often apparent how to write these sort of contracts. In this episode we review a blockchain devkit accelerator that can help generate the contracts from simple JSON based descriptions.     

Code: https://github.com/Azure-Samples/blockchain/tree/master/blockchain-development-kit/accelerators/registry-generator

Gradient Descent is the workhorse behind much of Machine Learning. When you fit a machine learning method to a training dataset, you’re almost certainly using Gradient Descent.

The process can optimize parameters in a wide variety of settings. Since it’s so fundamental to Machine Learning, Josh Starmer of StatQuest decided to make a “step-by-step” video that shows exactly how it works.

Heads up: there is some singing.

Leverage partner built, open sourced, Solution Accelerators to expedite your IoT solution development. Several Microsoft partners have developed solutions ranging from edge video analytics, to digital signage, to remote well monitoring for oil and gas. These are published under our partner’s GitHub repositories and free for anyone to use, rebrand, or even resell.

Visit the Device Partner Center: https://devicepartner.microsoft.com

Discover the 3rd party solution accelerators: https://aka.ms/3psa

Try out the IoT solution accelerators: https://www.azureiotsolutions.com/Accelerators

Create a Free Account (Azure): https://aka.ms/aft-iot

In this episode Frank and Andy talk with Milena Rodban, a Geopolitical Risk Consultant and Simulation Designer, which might be the best job title ever.Milena discusses the importance of data and the often overlooked security aspects around data, especially when set against the backdrop of complex business and security environments.

Press the play button below to listen here or visit the show page at DataDriven.tv

This second part of the tutorial session from SciPy 2018 provides an introduction to machine learning and scikit-learn “from the ground up” –starting with core concepts of machine learning, some example uses of machine learning, and how to implement them using scikit-learn. Going in detail through the characteristics of several methods, Andreas Mueller and Guillaume Lemaitre discuss how to pick an algorithm for your application, how to set its hyper-parameters, and how to evaluate performance.

Link to materials

Oliver Cameron is the Co-Founder and CEO of Voyage. Before that he was the lead of the Udacity Self-Driving Car program that made ideas in autonomous vehicle research and development accessible to the world. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on the GitHub repo.

This tutorial from SciPy 2018 provides an introduction to machine learning and scikit-learn “from the ground up” –starting with core concepts of machine learning, some example uses of machine learning, and how to implement them using scikit-learn.

Going in detail through the characteristics of several methods, Andreas Mueller and Guillaume Lemaitre discuss how to pick an algorithm for your application, how to set its hyper-parameters, and how to evaluate performance.