There are situations where data relevant to a machine learning problem are distributed among multiple locations that cannot share the data due to regulatory, competitiveness, or privacy reasons. For example, data present in users' cellphones, manufacturing data of companies in a given industrial sector, or medical records located at different hospitals. Moreover, participating sites often have different data distributions and computational capabilities. Federated Learning provides an approach to learn a joint model over all the available data in these environments. In this paper, we introduce a novel distributed validation weighting scheme (DVW), which evaluates the performance of a learner in the federation against a distributed validation set. Each learner reserves a small portion (e.g., 5%) of its local training examples as a validation dataset and allows other learners models to be evaluated against it. We empirically show that DVW results in better performance compared to established methods, such as FedAvg, both under synchronous and asynchronous communication protocols in data and computationally heterogeneous environments.

A major drawback of cross-network recommender solutions is that they can only be applied to users that are overlapped across networks. Thus, the non-overlapped users, which form the majority of users are ignored. As a solution, we propose CnGAN, a novel multi-task learning based, encoder-GAN-recommender architecture. The proposed model synthetically generates source network user preferences for non-overlapped users by learning the mapping from target to source network preference manifolds. The resultant user preferences are used in a Siamese network based neural recommender architecture. Furthermore, we propose a novel user based pairwise loss function for recommendations using implicit interactions to better guide the generation process in the multi-task learning environment.We illustrate our solution by generating user preferences on the Twitter source network for recommendations on the YouTube target network. Extensive experiments show that the generated preferences can be used to improve recommendations for non-overlapped users. The resultant recommendations achieve superior performance compared to the state-of-the-art cross-network recommender solutions in terms of accuracy, novelty and diversity.

Students' perception of classes measured through their opinions on teaching surveys allows to identify deficiencies and problems, both in the environment and in the learning methodologies. The purpose of this paper is to study, through sentiment analysis using natural language processing (NLP) and machine learning (ML) techniques, those opinions in order to identify topics that are relevant for students, as well as predicting the associated sentiment via polarity analysis. As a result, it is implemented, trained and tested two algorithms to predict the associated sentiment as well as the relevant topics of such opinions. The combination of both approaches then becomes useful to identify specific properties of the students' opinions associated with each sentiment label (positive, negative or neutral opinions) and topic. Furthermore, we explore the possibility that students' perception surveys are carried out without closed questions, relying on the information that students can provide through open questions where they express their opinions about their classes.

The massive size of modern neural networks has motivated substantial recent interest in neural network quantization. We introduce Stochastic Markov Gradient Descent (SMGD), a discrete optimization method applicable to training quantized neural networks. The SMGD algorithm is designed for settings where memory is highly constrained during training. We provide theoretical guarantees of algorithm performance as well as encouraging numerical results.

Biased regularization and fine-tuning are two recent meta-learning approaches. They have been shown to be effective to tackle distributions of tasks, in which the tasks' target vectors are all close to a common meta-parameter vector. However, these methods may perform poorly on heterogeneous environments of tasks, where the complexity of the tasks' distribution cannot be captured by a single meta-parameter vector. We address this limitation by conditional meta-learning, inferring a conditioning function mapping task's side information into a meta-parameter vector that is appropriate for that task at hand. We characterize properties of the environment under which the conditional approach brings a substantial advantage over standard meta-learning and we highlight examples of environments, such as those with multiple clusters, satisfying these properties. We then propose a convex meta-algorithm providing a comparable advantage also in practice. Numerical experiments confirm our theoretical findings.

A decade ago, a veggie burger would not have dominated the news cycle. But when the Beyond Meat Burger launched in 2016, it made some serious waves. This year, the company became the first vegan meat producer to launch an initial public offering (IPO), with trading opening at US$46 a share, 84 percent above its initial stock price. And with a slew of competitors nipping at its heels, Beyond Meat is leading a trend. In some ways, this new generation of meat alternatives is hyper-natural, made from all-plant proteins.

Artificial intelligence (AI) technology has been around for decades. However, we really didn't realize its potential until about a decade ago. Since then, the planet saw an exponential demand for AI engineers. As the ongoing tech talent shortage shows no signs of improving, it has provided software engineers (who are also in high demand) an opportunity to make the transition and fill the talent gap. To shed some light on transitioning from software engineering to AI engineering, I reached out to Cognitive Implementation Engineer at Ipsoft, Sasho Andrijeski and Codementor developer, Jayen Ashar. People make the transition for many different reasons; for some, it was a childhood passion.

On the first day of school at Weaverville Elementary, third-grade teacher Saundra Murphy asked the 14 boys and girls in her class if anyone could define the phrase "social distancing." "Social distancing means staying 6 feet or more away from each other," said a boy in a "Minecraft" T-shirt, his voice muffled by a camouflage face mask. "Does that mean we don't like the person?" The students shook their heads. "Does that mean we're just trying to be safe and respectful of our situation?"

Created by Stephen Grider English [Auto-generated], Indonesian [Auto-generated] Students also bought The Modern GraphQL Bootcamp (with Node.js and Apollo) Socket.IO (with websockets) - the details. In the coming years, there won't be a single industry in the world untouched by Machine Learning. A transformative force, you can either choose to understand it now, or lose out on a wave of incredible change. You probably already use apps many times each day that rely upon Machine Learning techniques. So why stay in the dark any longer?

In Chapter 1 you will build your first deep learning model, and by the end of the book you will know how to read and understand the Methods section of any deep learning paper. Python is a powerful, dynamic language. Rather than bake everything into the language, it lets the programmer customize it to make it work for them.