To keep a certain level of innovation in the classroom, teachers and schools
often turn to grant sources – especially for new technology. Finding the
right source and winning the grant can make your school’s language learning
dreams come true. But what are these sources and how do you gear up for
your grant application?
English Language Learners (ELLs), are a vastly diverse group of approximately 5 million students who speak a primary language other than English and who are not yet proficient in English, which is a second or additional language for them. In order to keep up with their native English speaking peers, ELLs need more instructional time and specialized instruction, including specially designed material and state-of-the-art educational tools to accelerate their learning.
Published By: Red Hat
Published Date: Jun 26, 2019
When any organization starts planning for cloud-native applications, it is important to consider
the entire time span: from selecting a development platform until an application is truly production-grade and ready for delivery in the cloud. It can be a long journey, with many decisions
along the way that can help or hinder progress.
For example, at the beginning of a move to cloud-native development, it is easy for inefficiencies
to occur if developers begin selecting tools and frameworks before they know where the application will be deployed. While enterprise developers want choice of runtimes, frameworks, and
languages, organizations need standards that address the entire application life cycle in order
to reduce operational costs, decrease risks, and meet compliance requirements. Organizations
also want to avoid lock-in, whether it is to a single provider of cloud infrastructure or the latest
In addition, given the steep learning curve in cloud development, con
Advances in deep neural networks have ignited a new wave of algorithms and tools for data scientists to tap into their data with artificial intelligence (AI). With improved algorithms, larger data sets, and frameworks such as TensorFlow, data scientists are tackling new use cases like autonomous driving vehicles and natural language processing. Read this technical white paper to learn reasons for and benefits of an end-to-end training system. It also shows performance benchmarks based on a system that combines the NVIDIA® DGX-1™, a multi-GPU server purpose-built for deep learning applications and FlashBlade, a scale-out, high performance, dynamic data hub for the entire AI data pipeline.
When augmenting the benefits package
for your organization, it’s natural to focus
on traditional perks that employees have come to
expect: PTO, health insurance, and maybe a tuition
assistance credit here or there. But if you’re looking
for creative and effective ways to stimulate
employee engagement while also driving business
results, you’ll want to consider the powerful impact
of offering language-learning opportunities.
Why language learning? It offers immediate and
long-term benefits to both employees and employers.
Research shows that organizations that offer access
to language learning see an increase in employee
engagement factors like loyalty, morale, and
productivity, which in turn boosts business performance
factors such as customer satisfaction
and internal communications.
Where’s the connection? And how can you reproduce
these benefits within your organization? This
playbook offers a deeper look at why language
learning has such a positive influence on employee
Proving the value of language training to business can be challenging so we decided to take the guesswork out of it. We surveyed 56,000 of our business product users to gain key insights into how language training has changed their businesses. This infographic shares the key results to help human resource, learning and development, and business line leaders better understand how language impacts business.
Published By: Microsoft
Published Date: Jul 20, 2018
Although AI research has been ongoing for decades, the past few years have seen
a leap in practical innovations, catalyzed by vast amounts of digital data, online
services, and enormous computing power. As a result, technologies such as
natural-language understanding, sentiment analysis, speech recognition, image
understanding, and machine learning have become accurate enough to power
applications across a broad range of industries.
LOOK TO RELEVANT USE CASES FOR YOUR BUSINESS. While use cases vary across industries, the most common ones fall into these categories and are usually associated with the listed neural network:
• Image classification or object detection: convolutional neural network (CNN) • Time-series predictions: long short-term memory (LSTM) • Natural language processing: recurrent neural network (RNN) • Unlabeled data classification and data labeling: autoencoder (AE) • Anomaly detection: autoencoder (AE) • Recommender systems: multilayer perceptron (MLP)
Work with your deep learning talent or consultants to identify which use cases best match your organization and desired solutions. Then recreate a successful, already proven method.
In this informational webinar, we focus on identifying funding streams for K-12 ELL and world language programs. Hear perspectives and insights on funding streams in K-12 education from funding expert David DeSchryver, Senior Vice President of Education Policy at Whiteboard Advisors.
ELLs are a diverse group of about ?ve million students in the U.S. who speak a primary language other than English and are not yet pro?cient in English. To catch and keep up with their native English speaking peers, ELLs need more instructional time and specialized instruction, including specially designed materials and advanced educational tools to accelerate their learning.
Well utilized state-of-the-art technology tools can transform not only learning but academic success itself for ELLs and their families.
To read the complete white paper and learn more about Rosetta Stone, go to k12hub.rosettastone.com.
How can you open your analytics program to all
types of programming languages and all levels of
users? And how can you ensure consistency across
your models and your resulting actions no matter
where they initiate in the company?
With today’s analytics technologies, the conversation
about open analytics and commerical analytics is no
longer an either/or discussion. You can now combine
the benefits of SAS and open source analytics
technology systems within your organization.
As we think about the entire analytics life cycle, it’s
important to consider data preparation, deployment,
performance, scalability and governance, in addition
to algorithms. Within that cycle, there’s a role for
open source and commercial analytics.
For example, machine learning algorithms can
be developed in SAS or Python, then deployed in
real-time data streams within SAS Event Stream
Processing, while also integrating with open systems
through Java and C APIs, RESTful web services,
Apache Kafka, HDFS and more.
Artificial intelligence (AI) leads the charge in the current
wave of digital transformation underway at many global
companies. Organizations large and small are actively
expanding their AI footprints as executives try to comprehend
more fully what AI is and how they can use it to capitalize
on business opportunities by gaining insight to the data
they collect that enables them to engage with customers
and hone a competitive edge. But, while AI may indeed be
the frontier of enterprise technology, there remain many
misconceptions about it.
Part of the confusion stems from the fact that AI is an
umbrella term that covers a range of technologies —
including machine learning, computer vision, natural language
processing, deep learning, and more — that are in various
stages of development and deployment. The use of AI for
dynamic pricing and targeted marketing has been in use for
a while, but actual AI computing where machines think like
humans is still many years from becoming mainstream. T
With all of the attention on machine learning, many are seeking a better understanding of this hot topic and the benefits that it could provide to their organizations. Machine learning – as well as deep learning, natural language processing and cognitive computing – are driving innovations in identifying images, personalizing marketing campaigns, genomics, and navigating the self-driving car. This e-book provides a primer on these innovative techniques as well as 10 best practices and a checklist for machine learning readiness.
Designing the most comprehensive language program involves proven and cost-effective technology. Rosetta Stone's Language Learning Suite for K-12 has interactive solutions that complement any language curriculum. Take a look at the infographic to discover how the rise of technology enhances instruction and encourages learning.
Technology can be used to improve educational outcomes for
ELLs and to promote school engagement for their families.
Technology tools can help accelerate language development
while minimizing the digital divide and the “learning gap” for
ELLs and their families.
By 2030, nearly one in five members of the workforce will be an immigrant.
How can we ensure that this population, so vital to maintaining a strong competitive US workforce, will gain the English-language skills necessary for job success? Adult ELL programs in high school districts and community colleges offer the most effective learning paths for non-native English speakers seeking to level the playing field.