Knowledge Transfer has been applied in solving a wide variety of problems. For example, knowledge can be transferred between tasks (e.g., learning to handle novel situations by leveraging prior knowledge) or between agents (e.g., learning from others without direct experience). Without loss of generality, we relate knowledge transfer to KL-divergence minimization, i.e., matching the (belief) distributions of learners and teachers. The equivalence gives us a new perspective in understanding variants of the KL-divergence by looking at how learners structure their interaction with teachers in order to acquire knowledge. In this paper, we provide an in-depth analysis of KL-divergence minimization in Forward and Backward orders, which shows that learners are reinforced via on-policy learning in Backward. In contrast, learners are supervised in Forward. Moreover, our analysis is gradient-based, so it can be generalized to arbitrary tasks and help to decide which order to minimize given the property of the task. By replacing Forward with Backward in Knowledge Distillation, we observed +0.7-1.1 BLEU gains on the WMT'17 De-En and IWSLT'15 Th-En machine translation tasks.

Unsupervised domain adaptation is effective in leveraging rich information from a labeled source domain to an unlabeled target domain. Though deep learning and adversarial strategy made a significant breakthrough in the adaptability of features, there are two issues to be further studied. First, hard-assigned pseudo labels on the target domain are arbitrary and error-prone, and direct application of them may destroy the intrinsic data structure. Second, batch-wise training of deep learning limits the characterization of the global structure. In this paper, a Riemannian manifold learning framework is proposed to achieve transferability and discriminability simultaneously. For the first issue, this framework establishes a probabilistic discriminant criterion on the target domain via soft labels. Based on pre-built prototypes, this criterion is extended to a global approximation scheme for the second issue. Manifold metric alignment is adopted to be compatible with the embedding space. The theoretical error bounds of different alignment metrics are derived for constructive guidance. The proposed method can be used to tackle a series of variants of domain adaptation problems, including both vanilla and partial settings. Extensive experiments have been conducted to investigate the method and a comparative study shows the superiority of the discriminative manifold learning framework.

This book has my strongest recommendation both as an introduction to Python as well as Data Science. A great introduction to IBM Watson and the services it provides!"

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Schools across the United States are facing shortages and long delays, of up to several months, in getting this year's most crucial back-to-school supplies: the laptops and other equipment needed for online learning, an Associated Press investigation has found. The world's three biggest computer companies, Lenovo, HP and Dell, have told school districts they have a shortage of nearly 5 million laptops, in some cases exacerbated by Trump administration sanctions on Chinese suppliers, according to interviews with over two dozen U.S. schools, districts in 15 states, suppliers, computer companies and industry analysts. As the school year begins virtually in many places because of the coronavirus, educators nationwide worry that computer shortfalls will compound the inequities -- and the headaches for students, families and teachers. "This is going to be like asking an artist to paint a picture without paint. You can't have a kid do distance learning without a computer," said Tom Baumgarten, superintendent of the Morongo County School District in California's Mojave Desert, where all 8,000 students qualify for free lunch and most need computers for distance learning.

Artificial Intelligence has been creating a buzz for some time, along with Machine learning and Big Data. It is transforming companies and competitive sectors, but it has only started to understand its educational applications. Today we can see AI's impact both in classroom and campus management. A considerable number of academics are exploring new technologies powered by artificial intelligence, but many of these advancements aren't ready for its grandeur. With promising results of the latest AI discoveries and advancements, we can now look forward to a substantial application in transforming higher education.

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From analyzing large volumes of data to building contact tracing applications or using machine learning algorithms to discover effective treatments for COVID-19 quickly, the demand for data scientists with diverse skill sets and backgrounds has soared. While Glassdoor has ranked data science as one of the Top 10 Best Jobs in America every year since 2015, the field, unfortunately, remains dominated by men and often fails to attract female talent, with only 16% of women making up the data science workforce. It can be hard for women who are just starting out to know what their career paths should look like, especially during challenging times. Having spent the past eight years working in enterprise data science within the science, government and enterprise sectors, I've learned what it takes to stand out in a male-dominated field and how critical it is to show the impact of your work, understand which skills are important to hone and how to overcome imposter syndrome. Here are the four things I wish I knew before getting into the field of data science.