In the last few years, the interest around fairness in machine learning has been gaining a lot of momentum. Rightfully so: our models are becoming more and more prevalent in our daily lives, and their impact on the society at large is rapidly increasing. I believe that today more than ever, it is crucial to make sure that the models we develop treat us, humans, fairly. Taken from Moritz Hardt lecture notes. In this blog post I will try and answer those questions! There are many ways to measure fairness and it varies from problem to problem and human to human.

Event Description: In in our "cyber" age how do we do sex, love and reproduction? This seminar is an interdisciplinary dialogue among psychoanalysts, critical and cultural thinkers, writers and those interested in how our age of technology, consumer (re)production, including pornography, and mass social media has affected what psychoanalysts call "the subject," which is how each and every one of us is uniquely human. The seminar takes place over 2 days, commencing on Friday evening with a panel of invited speakers who will give short presentations, followed by audience discussion. The seminar continues on Saturday morning with the invited keynote speaker, Isabel Millar (see talk and bio below). This is followed by a roundtable discussion with the Friday evening panellists and the invited speaker, and the seminar will conclude with an audience Q&A session.

This tutorial is part of the Getting started with Watson OpenScale learning path. In this tutorial, you'll see how IBM Watson OpenScale can be used to monitor your artificial intelligence (AI) models for fairness and accuracy. You'll get a hands-on look at how Watson OpenScale will automatically generate a debiased model endpoint to mitigate your fairness issues and provides an explainability view to help you understand how your model makes its predictions. In addition, you'll see how Watson OpenScale uses drift detection. Drift detection will tell you when runtime data is inconsistent with your training data or if there is an increase the data that is likely to lead to lower accuracy.

Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.

AWS's Machine Learning includes three techniques, binary classification, multiclass classification, and regression. What we will do in this course is to look at these three machine learning techniques with three different data sets. To keep things interesting, we will use Kaggle's data sets for two of our examples. If you are new to machine learning, don't worry, you'll learn machine learning concepts along the way and I'll walk you through the AWS console. We will work our way through the six Amazon Machine Learning steps.

I am working in ML/AI field for 6 years and apart from technical skills that I acquired while working on the projects, I have also discussed various aspects of ML/AI with my non-technical colleagues, who have mostly been senior manager, VPs or CXOs. When I heard about "AI For Everyone" course, I was a bit reluctant in attending it as I thought I know most of the generic stuff that might have been talked in the course. Recently, one of my colleagues discussed with me a few topics covered in this course which intrigued me to get a fresh perspective on these topics. So, I recently attended this course on Coursera. My motivation to write this blog is to make sure that I have understood key aspects of this course and am able to make my non-technical colleagues and project stakeholders understand the benefits & limitations of using AI.

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As one of the multi-class, single-label classification datasets, the task is to classify grayscale images of handwritten digits (28 pixels by 28 pixels), into their ten categories (0 to 9). Let's build a Keras CNN model to handle it with the last layer applied with "softmax" activation which outputs an array of ten probability scores(summing to 1). Each score will be the probability that the current digit image belongs to one of our 10 digit classes. After that, you can train the model with integer targets, i.e. a one-dimensional array like Note this won't affect the model output shape, it still outputs ten probability scores for each input sample. We'll train a model on the combined works of William Shakespeare, then use it to compose a play in the similar style.

Learn the basics of AI and Machine Learning and understand how to improve your organization's experience and data optimization with the power of Augmented Intelligence. Provided will be an overview of an AI strategy and you will learn about our roadmap in AI, Natural language and Machine learning.

Cadence's Paul McLellan considers why high-performance compute, high-performance networks, and security will all be vital to the next wave of devices and the importance of optimization. Synopsys' Taylor Armerding points to some best practices for assessing your supply chain to find the weak links that could lead to a security breach, from why to make it a priority to what to ask software vendors. Mentor's Tarek Ramadan checks out what's different in layout vs. schematic verification for high density advanced packaging designs. A National Instruments writer finds teams competing to create a radio systems that collaboratively share spectrum and continue to operate reliably in contested spectral environments, plus the creation of the simulated testing environment. ANSYS' Emmanuel Follin looks at different ways simulation can help automotive companies reach the eight million miles of testing estimated to be needed by autonomous driving systems.