Social learning algorithms provide models for the formation of opinions over social networks resulting from local reasoning and peer-to-peer exchanges. Interactions occur over an underlying graph topology, which describes the flow of information and relative influence between pairs of agents. For a given graph topology, these algorithms allow for the prediction of formed opinions. In this work, we study the inverse problem. Given a social learning model and observations of the evolution of beliefs over time, we aim at identifying the underlying graph topology. The learned graph allows for the inference of pairwise influence between agents, the overall influence agents have over the behavior of the network, as well as the flow of information through the social network. The proposed algorithm is online in nature and can adapt dynamically to changes in the graph topology or the true hypothesis.

In this work we study statistical properties of graph-based clustering algorithms that rely on the optimization of balanced graph cuts, the main example being the optimization of Cheeger cuts. We consider proximity graphs built from data sampled from an underlying distribution supported on a generic smooth compact manifold $M$. In this setting, we obtain high probability convergence rates for both the Cheeger constant and the associated Cheeger cuts towards their continuum counterparts. The key technical tools are careful estimates of interpolation operators which lift empirical Cheeger cuts to the continuum, as well as continuum stability estimates for isoperimetric problems. To our knowledge the quantitative estimates obtained here are the first of their kind.

Deep Learning Prerequisites: Linear Regression in Python Data science: Learn linear regression from scratch and build your own working program in Python for data analysis. BESTSELLER 22,535 students enrolled Created by Lazy Programmer Inc.  English [Auto-generated], Spanish [Auto-generated] Preview this course  - GET COUPON CODE Free Coupon Discount Udemy Online Courses

I started out wanting to learn AI Object Detection in Computer Vision... Now even though I have a masters degree in electronic engineering (M.Eng). It was still challenging for me to figure out. I had a lot of questions like... If Ubuntu, what version 16.04, 18.04, What kernel do I need? If I am training, what format does my dataset need to be in?

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When Russia invaded Ukraine in February 2022, the images of tanks and troops amassing and then crossing the border could make it seem like little has changed in the world of warfare. However, as the fighting in Ukraine progressed it also became clear how conflicts today are developing in new and very different ways. Many of the images we have seen were captured by satellites in space or on mobile phones, and the sharing of these on social media has helped to shape public attitudes and been used to circumvent or undermine state-sponsored messaging. We have also seen hackers declare cyberwar on Russia. In the latest episode of the WORLD:we got this podcast series Dr Tim Stevens and Dr Kenneth Payne, who are both based in the School of Security Studies in our Faculty of Social Science & Public Policy, explore how cyber operations, social media and artificial intelligence are changing the face of war.

Welcome to "Introduction to Data Science Using Python" where you will set a good foot in the fields of Data Science and Machine Learning. I'm your instructor Ali Desoki and I start from scratch going clearly over all the points in the course along with hands-on practical exercises and projects to summarize all the skills you've learned. This course is designed for Beginners covering all Aspects of what you need to know to start in the fields of data science and machine learning with practice notebooks which summarize all the skills you've learned. At the end of this course, you will be able to analyze and manipulate data with python and be able to start your career in this field. The ideal student for this course is someone who looks to start in the mentioned fields from scratch.

Learn how to visualize spatial data in maps and charts. Manipulate, clean and transform data. Welcome to the'Spatial Data Visualization and Machine Learning in Python' course. In this course we will be building a spatial data analytics dashboard using bokeh and python. We will be visualizing our data in a variety of bokeh charts, which we will explore in depth.

Understanding how important representation, role models, and mentoring had been to my own career journey, I started a network to support other Amazon employees looking to pursue a career in machine learning (ML) and artificial intelligence (AI). Open to anyone working at Amazon, the global Women in ML/AI group hosts regular networking events and organizes panel discussions with industry experts on career development. To discuss learnings from our professional journey, I sat down with fellow board members, including senior documentation manager Michelle Luna, senior software development manager Anna Khabibullina and general manager and product lead Shubha Pant. Here are some of the advice we found invaluable when launching and building a career in the field. Luna, Khabibullina, Pant and I are all proof that there are many paths into ML and AI -- from the traditional and linear, to the more unconventional.

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