This tutorial is part of the 2021 Call for Code Global Challenge. In this tutorial, learn how to pull 40 years' of global satellite-based soil moisture data from the European Commission, then train a model to compute moisture trends to identify regions that have a high probability to dry out and have droughts. Copernicus is the European Union's Earth observation program, looking at our planet and its environment. It offers information services that draw from satellite Earth observation and in-situ (non-space) data. Vast amounts of global data from satellites and ground-based, airborne, and seaborne measurement systems provide information.

In this tutorial, you will learn the proper ways to extract your personal data from your Linkedin account and use Python to analyze and draw useful insights from it. If you don't have a Linkedin account, please run as fast as you can to Linkedin page to create one. Actually it's not a good habit to not have a Linkedin account in this modern world…lol Linkedin is one of the biggest social network out there, and the chances are you are proud Linkedin member (if not create one now-please). Linkedin gives you access you to your data and you can download and analyze this data to draw insights from it. Linkedin has a clear guide as to how to download your data.

GMRES is a powerful numerical solver used to find solutions to extremely large systems of linear equations. These systems of equations appear in many applications in science and engineering. Here we demonstrate a real-time machine learning algorithm that can be used to accelerate the time-to-solution for GMRES. Our framework is novel in that is integrates the deep learning algorithm in an in situ fashion: the AI-accelerator gradually learns how to optimizes the time to solution without requiring user input (such as a pre-trained data set). We describe how our algorithm collects data and optimizes GMRES. We demonstrate our algorithm by implementing an accelerated (MLGMRES) solver in Python. We then use MLGMRES to accelerate a solver for the Poisson equation -- a class of linear problems that appears in may applications. Informed by the properties of formal solutions to the Poisson equation, we test the performance of different neural networks. Our key takeaway is that networks which are capable of learning non-local relationships perform well, without needing to be scaled with the input problem size, making them good candidates for the extremely large problems encountered in high-performance computing. For the inputs studied, our method provides a roughly 2$\times$ acceleration.

This multiple session tutorial is designed to train researchers and practitioners to begin analyzing synchrophasor (i.e., PMU) and point on wave data. The course covers concepts from power engineering and data science, and will show attendees how to develop efficient workflows for analyzing and visualizing time series data at scale. The first day of the course will cover foundational concepts from power systems engineering, and will relate PMU data to physical properties of the grid. The session will discuss phasor calculation, and methods for using phasor data to compute frequency. We will close with a summary of best practices and lessons learned from using PMU data in industry.

A team of Carnegie Mellon University learning scientists are developing a tool that could change the way high school teachers and students approach their computer science classes. This month, Schmidt Futures announced that the team is one of the winners of their Futures Forum on Learning: Tools Competition. This award will fund tools to aid recovery from pandemic learning loss and advance the field of learning engineering. The tool, RecapCS, was created by Ember Liu and Neil Thawani with support from John Stamper, an assistant professor in the Human-Computer Interaction Institute. Liu and Thawani both graduated from the HCII's Master of Educational Technology and Applied Learning Science (METALS) program, which trains graduate students to become learning engineers and learning experience designers.

PDF | In this paper, we compare two artificial neural networks (ANNs) approaches designed to perform channel equalization for millimeter-wave (mmWave)... | Find, read and cite all the research you need on ResearchGate

Cryptocurrency is gaining more ground each year, which means the space demands an even higher level of understanding for anyone who wants to actually come out ahead. What was once a niche interest for very specific groups of investors will soon be accepted by MasterCard and Tesla, while PayPal started integrating the currency late last year. On top of that, crypto trading is surging in popularity as well, emerging as a niche stock market for people who want to experiment with investing from the comfort of their laptop. If you're new to the world of investing, or if you're a seasoned investor worried about losing your edge, The Quantitative Crypto Trading Strategies Bundle is definitely worth a look at $145. It offers intermediate to advanced training on every aspect of cryptocurrency training, from programming and sorting out risks to the implementation of long-term strategies.

With the success of AI/ML in areas like image recognition, medicine, and the web, why are we only seeing incremental gains in the supply chain? What's throttling AI's ability to drive optimal supply chain performance? Ranjit Notani, co-founder and CTO of One Network Enterprises, and Joe Bellini (COO), will explode common myths about AI/ML and why network strategies are essential to success in Supply Chain Control Towers. There's a new approach to automated decision-making and decision-support in the supply chain, that marries network learnings with the unique insights of users -- for a multiparty multi-tier process that creates optimal outcomes. This webinar will help you understand: • The limits of enterprise-centric technology and why you need a network-based control tower strategy • The 5 areas in the supply chain where you can and should use network AI/ML • The 7 Challenges in achieving the full benefit of AI in Supply Chain and how to overcome them • How guardrails and time horizons affect the results you can obtain with AI • How workbenches with predictive and prescriptive analytics streamline problem-solving • Why improving forecasts is not a panacea for your supply chain.

In an effort to increase standardization across the PyTorch ecosystem Facebook AI in a recent blog post told that they would be leveraging Facebook's open-source Hydra framework to handle configs, and also offer an integration with PyTorch Lightning. This post is about Hydra. If you are reading this post then I assume you are familiar with what are config files, why are they useful, and how they increase reproducibility. And you also know what a nightmare is argparse. In general, with config files you can pass all the hyperparameters to your model, you can define all the global constants, define dataset splits, and … without touching the core code of your project.

A great technological shift is on the verge of occurring very soon. Disruptive Artificial Intelligence technologies are going to change the world and human labor will be replaced by robot workers and the shift has in-fact started. This mind-blowing course introduces you to the concept of Artificial Intelligence usage in the workplace along with providing you practical examples of the different platforms that deploy the same for automation. You will learn about the numerous Human Resources tools and usage of these in Artificial Intelligence, along with sales-based AI tools that can help you close the deal. You will be also introduced to Virtual chatbots that look like humans and do all the automation and support work for you in any industry you are in.