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Machine Learning with Earth Engine Python and Colab


Machine Learning with Earth Engine Python and Colab Become an expert in machine learning, python, big geospatial data & land use land cover in google earth engine What you'll learn Description Welcome to the Machine Learning with Earth Engine Python and Colab course. This Earth Engine course is without a doubt the most comprehensive course for anyone who wants to apply machine learning in python using satellite data. Even if you have zero programming experience, this course will take you from beginner to mastery. The course includes HD video tutorials. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know to apply remote sensing and cloud computing for forest monitoring application.

Google Earth Engine for Machine Learning & Change Detection


Google Earth Engine for Machine Learning & Change Detection Students will gain access to and a thorough knowledge of the Google Earth Engine platform. Get introduced and advance JavaScript skills on Google Earth Engine platform. This course is designed to take users who use GIS for basic geospatial data/GIS/Remote Sensing analysis to perform more advanced geospatial analysis tasks using a variety of different data and applying Machine Learning state of the art algorithms. In addition to improving your skills in JavaScript, this course will make you proficient in Google Earth Engine for land use and land cover (LULC) mapping and change detection. As a result, you will be introduced to the exciting capabilities of Google Earth Engine which is a global leader for cloud computing in Geosciences!

[P] Introducing Juggernaut: a neural net that trains models from the browser with no JS, no servers • r/MachineLearning


I'm not sure why that's interesting that it has "no JS". Transpiling to Wasm with Emscripten does not remove JS from the equation. Wasm can't talk to the host environment (the browser) so it's still using Javascript to get data in or out - Wasm has an import/export syntax for calling out to JS. It's still just interpreted on the browser engine; and, since it's in such a rough state (the'MVP' release only having been finalized in March), it's actually un-JITted. No worries there (I wasn't sure whether you thought that might go away.

e-Book: Machine Learning and Recommendation Engine


Building a simple but powerful recommendation system is much easier than you think. This guide explains innovations that make machine learning practical for business production settingsand demonstrates how even a small-scale development team can design an effective large-scale recommender. In this guide, Practical Machine Learning: Innovations in Recommendation, authors and Mahout committers Ted Dunning and Ellen Friedman shed light on a more approachable recommendation engine design and the business advantages for leveraging this innovative implementation style.

Accessible Artificial Intelligence from Penn - Penn AI


We are pleased to launch PennAI – an accessible artificial intelligence system and open-source software developed by at the University of Pennsylvania's Perelman School of Medicine by faculty, staff, and students from the Penn Institute for Biomedical Informatics (IBI). The components of PennAI include a human engine (i.e., the user); a user-friendly interface for interacting with the AI; a machine learning engine for data mining; a controller engine for launching jobs and keeping track of analytical results; a graph database for storing data and results (i.e., the memory); an AI engine for monitoring results and automatically launching or recommending new analyses; and a visualization engine to displaying results and analytical knowledge. This AI system provides a comprehensive set of integrated components for automated machine learning (AutoML), thus providing a data science assistant for generating useful results from large and complex data problems. More details can be found in our PennAI publications.