Few-shot Learning aims to learn classifiers for new classes with only a few training examples per class. Existing meta-learning or metric-learning based few-shot learning approaches are limited in handling diverse domains with various number of labels. The meta-learning approaches train a meta learner to predict weights of homogeneous-structured task-specific networks, requiring a uniform number of classes across tasks. The metric-learning approaches learn one task-invariant metric for all the tasks, and they fail if the tasks diverge. We propose to deal with these limitations with meta metric learning. Our meta metric learning approach consists of task-specific learners, that exploit metric learning to handle flexible labels, and a meta learner, that discovers good parameters and gradient decent to specify the metrics in task-specific learners. Thus the proposed model is able to handle unbalanced classes as well as to generate task-specific metrics. We test our approach in the `$k$-shot $N$-way' few-shot learning setting used in previous work and new realistic few-shot setting with diverse multi-domain tasks and flexible label numbers. Experiments show that our approach attains superior performances in both settings.

As online systems based on machine learning are offered to public or paid subscribers via application programming interfaces (APIs), they become vulnerable to frequent exploits and attacks. This paper studies adversarial machine learning in the practical case when there are rate limitations on API calls. The adversary launches an exploratory (inference) attack by querying the API of an online machine learning system (in particular, a classifier) with input data samples, collecting returned labels to build up the training data, and training an adversarial classifier that is functionally equivalent and statistically close to the target classifier. The exploratory attack with limited training data is shown to fail to reliably infer the target classifier of a real text classifier API that is available online to the public. In return, a generative adversarial network (GAN) based on deep learning is built to generate synthetic training data from a limited number of real training data samples, thereby extending the training data and improving the performance of the inferred classifier. The exploratory attack provides the basis to launch the causative attack (that aims to poison the training process) and evasion attack (that aims to fool the classifier into making wrong decisions) by selecting training and test data samples, respectively, based on the confidence scores obtained from the inferred classifier. These stealth attacks with small footprint (using a small number of API calls) make adversarial machine learning practical under the realistic case with limited training data available to the adversary.

Is there any truth to the notion that college instructors who implement active learning receive lower teaching evaluations? Henderson et al. present data from college physics instructors who attended a new-faculty workshop and attempted to incorporate active learning into their introductory course. Contrary to common belief, 48% of these instructors reported an increase in student evaluations, 32% reported no change, and only 20% reported a decrease in their evaluations. The authors acknowledge the limitations of the study, including the nature of self-reported data as well as changes in student evaluations over time, yet provide the overall recommendation that instructors (and institutions) should not let perceived anxiety over negative student evaluations be a reason to avoid implementing evidence-based teaching practices.

This tutorial explains matplotlib's way of making plots in simplified parts so you gain the knowledge and a clear understanding of how to build and modify full featured matplotlib plots. Matplotlib is the most popular plotting library in python. Using matplotlib, you can create pretty much any type of plot. However, as your plots get more complex, the learning curve can get steeper. The goal of this tutorial is to make you understand'how plotting with matplotlib works' and make you comfortable to build full-featured plots with matplotlib. The following piece of code is found in pretty much any python code that has matplotlib plots.

The UiPath Basic Concept Series, the first RPA tutorial for beginners, introduces you to the three main products that make up our RPA platform: UiPath Studio, UiPath Robot, and UiPath Orchestrator. UiPath Studio is an advanced tool that allows users to design automation processes in a visual way through diagrams. UiPath Robot then executes those processes either unattended (without human supervision) or attended (with a human's action triggering the process). This initial tutorial covers the multiple types of workflows and activities available in Studio (sequences, flowcharts, and transactional business processes) to tailor your process to your needs. It also teaches you the basics of recorder functionality, which is often the easiest way to create workflows.

Before discussing the ways in which you can learn more about machine learning, we would like to discuss, what the subject matter actually is. Machine learning is basically teaching a computer how to make decisions with the help of relevant data. It is very important for the computer to be able to understand patterns without being fully programmed. The demand for machine learning is an all-time high. It is a skill set which you want to possess, especially in this computer savvy era.

Despite detailed analysis being conducted around the benefits of artificial intelligence (AI) in various industries, its effect on education has been relatively unexplored. Global innovation foundation Nesta has begun a research project to explore the future of AI in education and found a relatively modest – but fast-growing – bank of academic literature focusing on the topic. As the literature on AI in education grows, however, they also expect to see its scope widening. Early academic literature was, typically, focused on how AI could be used to solve'Bloom's 2-Sigma Problem' and replicate the'gold standard' of education: one-to-one tutoring. However, academics, researchers and technologists are now describing experiments where AI is focused on whole range of different elements – from enabling collaboration between peers to assessing complicated skills, like creativity.

Ravi Adve from University of Toronto graciously invited me to give a public lecture at the university earlier this summer. I was very grateful for the opportunity. The university's facilities were fantastic, and Ravi did a wonderful job organizing everything. The audience was engaging and asked thoughtful questions, and the attendance was much higher than I had anticipated for a Tuesday morning lecture in the middle of summer! There was great representation from multiple departments, including Electrical and Computer Engineering, Computer Science, and Mathematics.

Care and Feeding is Slate's parenting advice column. In addition to our traditional advice, every Thursday we feature an assortment of teachers from across the country answering your education questions. Have a question for our teachers? Email askateacher@slate.com or post it in the Slate Parenting Facebook group. This week's Ask a Teacher panel: Matthew Dicks, fifth grade, Connecticut Cassy Sarnell, preschool special education, New York Carrie Bauer, middle and high school, New York Amy Scott, eighth grade, North Carolina My fourth-grade daughter is a joy to be around, a good friend, and a well-behaved student.

A modeling averaging ensemble combines the prediction from each model equally and often results in better performance on average than a given single model. Sometimes there are very good models that we wish to contribute more to an ensemble prediction, and perhaps less skillful models that may be useful but should contribute less to an ensemble prediction. A weighted average ensemble is an approach that allows multiple models to contribute to a prediction in proportion to their trust or estimated performance. In this tutorial, you will discover how to develop a weighted average ensemble of deep learning neural network models in Python with Keras. How to Develop a Weighted Average Ensemble for Deep Learning Neural Networks Photo by Simon Matzinger, some rights reserved. Model averaging is an approach to ensemble learning where each ensemble member contributes an equal amount to the final prediction. In the case of regression, the ensemble prediction is calculated as the average of the member predictions.