educational technology

Artificial intelligence on Hadoop: Does it make sense? ZDNet


This week MapR announced a new solution called Quick Start Solution (QSS), focusing on deep learning applications. MapR touts QSS as a distributed deep learning (DL) product and services offering that enables the training of complex deep learning algorithms at scale. Ted Dunning, MapR chief application architect, explains: "The best approach for pursuing AI/Deep learning is to deploy a scalable converged data platform that supports the latest deep learning technologies with an underlying enterprise data fabric with virtually limitless scale." But being able to run ML or DL on Hadoop does not really make a Hadoop vendor an AI vendor too.

Brace yourselves: AI is set to explode in the next 4 years


A new report predicts that artificial intelligence (AI) in the U.S. education sector will grow 47.5 percent through 2021. The report, Artificial Intelligence Market in the U.S. Education Sector 2017-2021, is based on in-depth market analysis with inputs from industry experts. Because games have the potential to engage students while teaching them challenging education concepts in an engaging manner, vendors are incorporating AI features into games to enhance their interactivity. Educational games that include adaptive learning features give students frequent and timely suggestions for a guided learning experience.

Why Machine Learning is the new technology breakthrough Letimo


Machine learning is a process where computer algorithms find patterns in data, and then predict probable outcomes of that data. Machine learning programs build a model from sample inputs and then predict the outputs of those data inputs. Machine learning is a type of artificial intelligence (AI). It analyzes past behavior and shows articles that relate to that behavior, so that it can provide a personalized experience for the user.

First Deep Learning for coders MOOC launched by Jeremy Howard


Deep Learning For Coders is a new online course that, for the first time, promises to teach coders how to create state of the art deep learning models. Jeremy says that this is First deep learning course to show end-to-end how to get state of the art results (including how to get a top place in a Kaggle competition) First code-centric full deep learning course (18 hours of lessons) First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet! First deep learning course to show end-to-end how to get state of the art results (including how to get a top place in a Kaggle competition) First code-centric full deep learning course (18 hours of lessons) First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet! First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet!

Machine Learning for Data Science - Udemy


In this introductory course, the "Backyard Data Scientist" will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the "techno sphere around us", why it's important now, and how it will dramatically change our world today and for days to come. We'll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science: To make sense of the Machine part of Machine Learning, we'll explore the Machine Learning process: Our final section of the course will prepare you to begin your future journey into Machine Learning for Data Science after the course is complete. So I invite you to join me, the Backyard Data Scientist on an exquisite journey into unlocking the secrets of Machine Learning for Data Science.... for you know - everyday people... like you!

The Future of Jobs and Jobs Training


Automation, robotics, algorithms and artificial intelligence (AI) in recent times have shown they can do equal or sometimes even better work than humans who are dermatologists, insurance claims adjusters, lawyers, seismic testers in oil fields, sports journalists and financial reporters, crew members on guided-missile destroyers, hiring managers, psychological testers, retail salespeople, and border patrol agents. A recent study by labor economists found that "one more robot per thousand workers reduces the employment to population ratio by about 0.18-0.34 When Pew Research Center and Elon University's Imagining the Internet Center asked experts in 2014 whether AI and robotics would create more jobs than they would destroy, the verdict was evenly split: 48% of the respondents envisioned a future where more jobs are lost than created, while 52% said more jobs would be created than lost. This survey noted that employment is much higher among jobs that require an average or above-average level of preparation (including education, experience and job training); average or above-average interpersonal, management and communication skills; and higher levels of analytical skills, such as critical thinking and computer skills. A focus on nurturing unique human skills that artificial intelligence (AI) and machines seem unable to replicate: Many of these experts discussed in their responses the human talents they believe machines and automation may not be able to duplicate, noting that these should be the skills developed and nurtured by education and training programs to prepare people to work successfully alongside AI.

Ensemble Machine Learning in Python: Random Forest, AdaBoost


We've already learned some classic machine learning models like k-nearest neighbor and decision tree. In this course you'll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the base models they are made of. In particular, we will study the Random Forest and AdaBoost algorithms in detail. Since deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.

Machine Learning in A Year, by Per Harald Borgen - Dataconomy


A few weeks in, I wanted to learn how to actually code machine learning algorithms, so I started a study group with a few of my peers. The most important takeaway from this period was the leap from non-vectorized to vectorized implementations of neural networks, which involved repeating linear algebra from university. So I asked my manager if I could spend some time learning stuff during my work hours as well, which he happily approved. Having gotten a basic understanding of neural networks at this point, I wanted to move on to deep learning.

NVIDIA to train 100,000 developers on deep learning - SD Times


"There is a real demand for developers who not only understand artificial intelligence, but know how to apply it in commercial applications," said Christian Plagemann, vice president of Content at Udacity, who will be working with the institute on self-driving car content. "NVIDIA is a leader in the application of deep learning technologies and we're excited to work closely with their experts to train the next generation of artificial intelligence practitioners." The Deep Learning Institute is also expanding to include new deep learning training labs, new coursework for educators, and new DLI certified training partners. The company will work with Microsoft Azure, IBM Power and IBM Cloud teams to bring the institute's content to cloud solutions.

Machine Learning: Regression Coursera


About this course: Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions.