Modern online services continuously generate data at very fast rates. This continuous flow of data encompasses content -- e.g., posts, news, products, comments --, but also user feedback -- e.g., ratings, views, reads, clicks --, together with context data -- user device, spatial or temporal data, user task or activity, weather. This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate online methods able to transparently adapt to the inherent dynamics of online services. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with the continuous flows of data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online. The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, and their implications regarding multiple dimensions, such as evaluation, reproducibility, privacy and explainability.

The 36th IEEE International Parallel and Distributed Processing Symposium (IPDPS) is calling for papers for its ExSAIS 2022: Workshop on Extreme Scaling of AI for Science which will take place on June 3, 2022. The due date for paper submissions is Feb. 1, 2022. The following description of the workshop and its submission guidelines are provided by the event's organizers: The evolution of machine perception to machine learning and reasoning, and ultimately machine intelligence, has the potential to significantly impact acceleration and advancement of autonomous scientific discovery and the operation of scientific instruments. While machine reasoning will enable intelligent systems to better understand and interact with their physical world, machine intelligence through modeling, simulation and automation, closes the gap between experiments, extreme computing, and scientific discovery. In order to usher in this new era of autonomous science, advances in several areas of artificial intelligence and other disciplines e.g., high-performance computing, data engineering need to come together.

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It's then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference.

The Augusta Webster, MD, Office of Medical Education (AWOME) has begun introducing a new component of the medical education curriculum to current medical students: instruction in Digital Health and Data Science. The curriculum is being co-developed by David Liebovitz, MD, associate vice chair for clinical informatics in the Department of Medicine and co-director of the Center for Medical Education in Data Science and Digital Health, and Mahesh Vaidyanathan, MD, MBA, assistant professor of Anesthesiology. The utilization of large data sets and machine learning is rapidly growing in healthcare. Feinberg is proud to be at the forefront of preparing our students to not only utilize this technology in care delivery and research, but also to critically evaluate its applicability and limitations. I am confident that this curriculum will be the foundation for many of our students to become leaders in the field of data science and augmented intelligence in medicine." The new curriculum component will see students meeting several core competencies and learning outcomes while learning about the health data ecosystem; the health IT regulatory environment; data science methods and research; digital health decision support; bias, ethics and health equity; and the sociotechnical context for digital health and data science. Mahesh Vaidyanathan, MD, MBA, assistant professor of Anesthesiology, is a co-leader of Feinberg's new Digital Health and Data Science curriculum component for medical students. "The tools that data science brings to clinical care enable more effective and personalized care for our patients.

Create a GAN capable of generating high resolution images using TensorFlow 2.0 · Distribute training on a TPU or multiple GPUS · Implement the R2 This course covers the fundamentals necessary for a state-of-the-art GAN. Anyone who experimented with GANs on their own knows that it's easy to throw together a GAN that spits out MNIST digits, but it's another level of difficulty entirely to produce photorealistic images at a resolution higher than a thumbnail. You'll create and train a GAN that can be used in real-world applications. And because training high-resolution networks of any kind is computationally expensively, you'll also learn how to distribute your training across multiple GPUs or TPUs. This allows students to train generators up to 512x512 resolution with no hardware costs at all.

Master Apache Spark - Hands On! - Learn how to slice and dice data using the next generation big data platform - Apache Spark! Some basic Java programming experience is required. A crash course on Java 8 lambdas is included You will need a personal computer with an internet connection. The software needed for this course is completely freely and I'll walk you through the steps on how to get it installed on your computer Some basic Java programming experience is required. You will need a personal computer with an internet connection.

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Readers will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this book; however, familiarity with at least one programming language is assumed.

Active inference is an account of cognition and behavior in complex systems which brings together action, perception, and learning under the theoretical mantle of Bayesian inference. Active inference has seen growing applications in academic research, especially in fields that seek to model human or animal behavior. While in recent years, some of the code arising from the active inference literature has been written in open source languages like Python and Julia, to-date, the most popular software for simulating active inference agents is the DEM toolbox of SPM, a MATLAB library originally developed for the statistical analysis and modelling of neuroimaging data. Increasing interest in active inference, manifested both in terms of sheer number as well as diversifying applications across scientific disciplines, has thus created a need for generic, widely-available, and user-friendly code for simulating active inference in open-source scientific computing languages like Python. The Python package we present here, pymdp (see https://github.com/infer-actively/pymdp), represents a significant step in this direction: namely, we provide the first open-source package for simulating active inference with partially-observable Markov Decision Processes or POMDPs. We review the package's structure and explain its advantages like modular design and customizability, while providing in-text code blocks along the way to demonstrate how it can be used to build and run active inference processes with ease. We developed pymdp to increase the accessibility and exposure of the active inference framework to researchers, engineers, and developers with diverse disciplinary backgrounds. In the spirit of open-source software, we also hope that it spurs new innovation, development, and collaboration in the growing active inference community.

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path. Most topics include hands-on Python code examples you can use for reference and for practice. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon.

DBT data build tool helps data teams work like software engineers, transform data and control the flow to ship trusted data, faster. It means that we first load the data as is to the target and then use SQL (DBT data build tool) to transform it. DBT data build tool will materialize your SQL selects into table views and manage the flow of executing the SQL. ETL developers, DBA, BI developers, decision-makers that consider DBT, SQL programmers, data analysts, data engineers.