Please cite as: Blake, A., & Doherty, I. (2007). An instructional design course for clinical educators: First iteration design research reflections. Journal of Learning Design, 2(2), 83-104. Available: www.jld.qut.edu.au/publications/vol2no2/documents/Blakean...
Per Josh Bersin "... I believe we are going through one of the most important transitions to corporate training in the last 10 years. While e-learning certainly forced organizations to invest and learn in many ways, today’s corporate training world is changing even more. A recession is once-again fueling the fire: organizations are consolidating their…
Caffe | Deep Learning Framework
The Mindful Learning Framework is an educational practice that teaches students how to learn deeply and with intention.
Who’s on top in usage, interest, and popularity?
According to a report published by BleepingComputer, a hacker duped some PyTorch machine-learning framework users into installing malware.
In September 2018, I compared all the major deep learning frameworks in terms of demand, usage, and popularity. TensorFlow was the champion of deep learning frameworks and PyTorch was the youngest framework. How has the landscape changed?
Over the last year or so we have talked with hundreds of companies about their desire to transform their corporate training programs to take advantage of social networking, knowledge management, communities of practice, and...
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One of my friends is the founder and Chief data scientist at a very successful deep learning startup. 2017 was a good year for his startup with funding and increasing adoption. However, on a Thursday
In this era of artificial intelligence, one might safely say that convolution is the heart of deep learning & computer vision. Several popular deep learning architectures, such as AlexNet, GoogleNet…
An Open Source Machine Learning Framework for Everyone - tensorflow/tensorflow
Explore Challenge Based Learning in the Apple Education Community and find videos and resources to help you bring the CBL Framework to your learners.
Researchers propose a deep neural network-based forward design space exploration using active transfer learning and data augmentation A new study
Yahoo have just released TensorFlowOnSpark (TFoS), its latest framework for distributed deep learning on big-data clusters, to the open source community. Nearly a year ago, Yahoo published CaffeOnSpark (CoS), an open source framework that allows distributed deep learning and big-data processing on identical Spark and Hadoop clusters. Yahoo uses CoS in many forms, and the…
This article presents a typical workflow for an algorithmic trading system on Energy markets and discusses some general considerations behind modelling choices in Machine Learning.
In recent years, service-oriented computing technology has developed rapidly. The growing number of services increases the choice burden of software developers when developing service-based systems, such as mashups or applications. How to recommend appropriate services for developers to create mashups has become a basic problem in service-oriented recommendation systems. To solve this problem, people have proposed various methods to recommend services to match the requirements of the new mashups and achieved great success. However, there are also some challenges in feature utilization and text requirement understanding. Therefore, we propose a Mashup-oriented Service Recommendation framework based on Deep Learning, called MSRDL. A content component was designed in MSRDL to generate the representation of mashups and services. Besides, an interaction component was created in MSRDL to model the invocation records between mashups and services. The output features of the two parts are further integrated into MLP to obtain the service recommendation lists. Experimental results on ProgrammableWeb datasets show that our method is superior to the state-of-the-art methods.
Facebook's Pythia deep learning framework, which is now available in open source, is designed to benchmark natural language processing and vision AI models.
A new machine learning framework could pave the way for small, mobile quantum networks.
This paper proposes a network model framework based on long and short-term memory (LSTM) and conditional random field (CRF) to promote Li-ion battery capacity prediction results. The model uses LSTM to extract temporal features from the data and CRF to build a transfer matrix to enhance temporal feature learning for long serialization prediction of lithium battery feature sequence data. The NASA PCOE lithium battery dataset is selected for the experiments, and control tests on LSTM temporal feature extraction modules, including recurrent neural network (RNN), gated recurrent unit (GRU), bi-directional gated recurrent unit (BiGRU) and bi-directional long and short term memory (BiLSTM) networks, are designed to test the adaptability of the CRF method to different temporal feature extraction modules. Compared with previous Li-ion battery capacity prediction methods, the network model framework proposed in this paper achieves better prediction results in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) metrics.
