In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability. This problem can be addressed by Distributed or Federated Learning (FL) that leverages a parameter server to aggregate model updates from individual participants. However, most existing Distributed or FL frameworks have overlooked an important aspect of participation: collaborative fairness. In particular, all participants can receive the same or similar models, regardless of their contributions. To address this issue, we investigate the collaborative fairness in FL, and propose a novel Collaborative Fair Federated Learning (CFFL) framework which utilizes reputation to enforce participants to converge to different models, thus achieving fairness without compromising the predictive performance. Extensive experiments on benchmark datasets demonstrate that CFFL achieves high fairness, delivers comparable accuracy to the Distributed framework, and outperforms the Standalone framework.

The gradient descent is the simplest optimisation algorithm represented by gradient dynamics in a potential. When the input data is finite, gradient descent dynamics fluctuates due to the finite size effects, and is called stochastic gradient descent. In this paper, we study stability of stochastic gradient descent dynamics from the viewpoint of dynamical systems theory. Learning is characterised as nonautonomous dynamics driven by uncertain input from the external, and as multi-scale dynamics which consists of slow memory dynamics and fast system dynamics. When the uncertain input sequences are modelled by stochastic processes, dynamics of learning is described by a random dynamical system. In contrast to the traditional Fokker-Planck approaches [5, 15], the random dynamical system approaches enable the study not only of stationary distributions and global statistics, but also of the pathwise structure of stochastic dynamics. Based on nonautonomous and random dynamical system theory, it is possible to analyse stability and bifurcation in machine learning.

Spun out of Dell’s Digital Innovation Lab in 2013, Predictive Science has benefited from $40M in total R&D investment to become one of the fastest growing tech startups in the United States. Predictive Science is helping companies such as Verizon, NFL, Neiman Marcus, Dell, VMware and many others unlock the power of their data by creating algorithms that are changing the world. Predictive Science is looking for a Data Scientist who can work with Fortune 1000 companies and other companies around the world to help them take on challenging data problems that can provide high impact results. This is a freelance data scientist position who will work with other senior data scientists to consult with executives and data scientists who are a part of the Predictive Science network. Desired Experience Advanced Experience with programming scripts such as Python, Java, Scala, C++ in Linux/Unix, and R. Preference that you are highly knowledgeable in numerous languages. Experience in creating and implementing machine learning algorithms and advanced statistics such as: regression, clustering, decision trees, exploratory data analysis methodology, simulation, scenario analysis, modeling, and neural networks. Experience with web services such as AWS ,DigitalOcean, Redshift, S3, and Spark. Also the ability to connect data using web API, REST API, and web crawling techniques. Experience with SQL querying and knowledge of PostgreSQL, MySQL, MSSQL databases. Experience in analyzing data from business line data applications and data providers such as Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, Nielsen, Comscore, Simmons, MRI and etc Experience in visualizing data to stakeholders in a simple and concise manner through visualization software such as ggplot, D3,Tableau Qlinkview, Periscope, Business Objects, or other similar software. Ability to analyze data, draw insights, and prepare reports in a cohesive, intuitive, and simplistic manner to the client. Strong communication skills Excellent organization and prioritization skills About the Opportunity Work with our Data Science team to implement big data solutions for the client Mine massive amounts and perform large-scale data analysis to extract useful business insights Identify actionable insights, suggest recommendations, and influence the direction of the business by effectively communicating results to cross functional groups Job Perks Being part of Predictive Science means more than just working on challenging projects. There are other benefits you will receive as well. Free Conference Passes: Join data science experts from all around the world for amazing learning events. The events are free for Predictive Science network members. Special Offers and Free Products: Predictive Science partners provide special offers and free products for network members. Get your hands on early-release products and get the opportunity to be an influencer. Recruiting Services: Looking to take the next big step in your career? Predictive Science offers headhunting services to network members. You will receive career and resume feedback, and head hunters will help you get placed. Leadership Opportunities: Not getting the leadership experience you want from your current job? Trying to grow your leadership abilities? Predictive Science offers industry leadership opportunities for network members. Continue to expand your skills and build your resume. Network Opportunities: Build relationships with other data scientists, vendors, and company executives. Expand your network and opportunities as you partner with network members on projects. Training: Predictive Science helps you get hands-on experience as a data scientist while you get paid and sets you on a high-growth-trajectory path. Your skills will be challenged all along the way, and opportunities will be provided through conferences and online training to help you learn new skills that will make you more valuable. Time Commitment Predictive Science gives you the flexibility of working remotely and picking what hours you want to work. Already have a job but want to earn more money? Predictive Science can be flexible to accommodate your situation. Want a lot of flexibility? Sign up for a project that pays you per task or per hour. If you want more dependability and a full time commitment, we can provide contractor or full-time position. We Want You to Succeed Predictive Science is committed to helping you grow your career and get the experience you need to become successful. Our goal is to build the largest network of data scientists and place them in the most successful positions. Your skills will be challenged all along the way, and opportunities will be provided through conferences and online training to help you learn new skills that will make you more valuable and get promoted. Additionally, to support your career growth, we have our own recruiting team that works hard for you to find a position either with Predictive Science or somewhere else. No other company cares more about your career progression than Predictive Science. About Predictive Science Predictive Science is the fastest growing data science network focused on building the largest data science platform that can help businesses turn data into actionable results while significantly creating cost savings. Challenging data problems that were too expensive, risky, or resource limited to solve, can now be solved with the help of Predictive Science and its global network of data scientists. Predictive Science is helping companies such as Verizon, NFL, Neiman Marcus, Dell, VMware and many others unlock the power of their data by creating algorithms that are changing the world.

Timed Practice Exam is coming soon! New reference architecture section with hands-on lab that demonstrates how to build a data lake solution using AWS Services and the best practices: 2020 AWS S3 Data Lake Architecture. This topic covers essential services and how they work together for a cohesive solution. AWS Artificial Intelligence material is now live! Within a few minutes, you will learn about algorithms for sophisticated facial recognition systems, sentiment analysis, conversational interfaces with speech and text and much more.

Artificial intelligence (AI) has become a revolution in the last few years, and the number of developers, graduate students, and even high schoolers exploring the field has exploded. This begs a fundamental question: When is it the right time to start learning AI? We hypothesized that teaching students AI from a young age, specifically in middle school, is a perfect time to start. After teaching an intro to AI/ML course this summer, we realized that middle schoolers are indeed able to understand AI/ML, though with a few caveats. Let's establish a few baselines of what most middle schoolers know.

The course will mostly focus on helping you implement different statistical analysis techniques on your data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects immediately! TAKE ACTION NOW:) You'll also have my continuous support when you take this course just to make sure you're successful with it. If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you're not completely satisfied with the course.

Expanding its use of artificial intelligence techniques to improve forecasting, the National Center for Atmospheric Research (NCAR) is taking part in a major national initiative to advance AI in research and education. NCAR will collaborate with the University of Oklahoma and other leading institutions on the new National Science Foundation (NSF) AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography. NSF announced an investment of more than $100 million to establish this institute and six others, the result of a national competition to support research and education hubs at U.S. colleges and universities. The goal of the AI institutes is to "bring together academia, industry, and government to unearth profound discoveries and develop new capabilities advancing American competitiveness for decades to come," said NSF director Sethuraman Panchanathan. NCAR will focus on conducting AI and risk communication research to better understand the Earth system and advance forecasts in ways that are most useful for helping society manage hazardous weather risks.

Live Online Workshop During the workshop, you'll learn the basics of data science using Python programming through examples provided by the instructor. No prior programming experience is necessary. You must join the webinar via a computer. In this live online workshop, you will learn the basics of machine learning through a hands-on example. The instructor will conduct a live demo and lead participants through how to predict your sales forecast using actual data.

You can also download code cheat sheets, checklists, and worksheets to shorten the data science learning curve. Want to level up your spreadsheet skills from intermediate to advanced? This course by Ben Collins teaches you one new high-level spreadsheet formula or technique every day for 30 days, using Google Sheets. These bite-sized tutorials will get you comfortable with manipulating data in spreadsheets in more complex ways.

This blog post was co-authored by Anny Dow, Product Marketing Manager, Azure Cognitive Services. As schools and organizations around the world prepare for a new school year, remote learning tools have never been more critical. Educational technology, and especially AI, has a huge opportunity to facilitate new ways for educators and students to connect and learn. Today, we are excited to announce the general availability of Immersive Reader, and shine a light on how new improvements to Azure Cognitive Services can help developers build AI apps for remote education that empower everyone. Immersive Reader is an Azure Cognitive Service within the Azure AI platform that helps readers read and comprehend text.