The graph represents a network of 1,882 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 01 April 2022 at 12:09 UTC. The requested start date was Friday, 01 April 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 8-hour, 15-minute period from Tuesday, 29 March 2022 at 15:45 UTC to Friday, 01 April 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

This course is a comprehensive guide to Generative Adversarial Networks (GANs). The theories are explained in-depth and in a friendly manner. GANs have been one of the most interesting developments in deep learning and machine learning recently. Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs. GAN stands for generative adversarial network, where 2 neural networks compete with each other.

Welcome to the Complete Deep Learning Course 2021 With 7 Real Projects. This course will guide you through how to use Google's TensorFlow framework This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!

Learn how to create state of the art neural networks for deep learning with Facebook's PyTorch Deep Learning library! Welcome to the best online course for learning about Pytorch! Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence. Is it possible that Tensorflow is popular only because Google is popular and used effective marketing? Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems?

The Complete iOS Machine Learning Masterclass is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is ... If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you'll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps.

Each year that passes sees artificial intelligence (AI) integrating even further into major institutions. It is set to contribute $15.7tn to the global economy by 2030, which exceeds the current output of China and India combined. This game-changing technology is becoming increasingly embedded in our daily lives, from the devices on our bodies that measure our heart rates to those in our home that play our music. With AI poised to play an increasingly important role in society, further regulations are likely to be introduced worldwide. Over the past five years, more than 60 countries have developed over 700 AI policy initiatives.

Often, artificial intelligence (AI) is used broadly to describe all types of systems that seem to make decisions we do not quite understand. But while many reasonably complex systems make decisions like this, it does not immediately make them "intelligent." For example, I might not understand how my "smart" oven thermometer seems to know when my roast beef will be perfectly done, or how my garden light knows when to turn on, but the engineers putting together the (not-too-complex) mathematical equation do. There are many other systems that, at first glance, look intelligent--but they are just constructed by smart people. We should not label these as "intelligent" because that suggests they are making their own decisions instead of simply following a human-designed path. A better way to distinguish (artificially) intelligent systems from those that just follow human-made rules is to look for the person who can explain the systems' inner workings (i.e., the person ultimately responsible for what the systems do).

At the end of the Course you will understand the basics of Artificial Neural Networks. At the end of the Course you will understand the basics of Artificial Neural Networks. The course will have step by step guidance for Artificial Neural network development in Python.

In this hands-on tutorial, we will provide you with a reimplementation of SimCLR self-supervised learning method for pretraining robust feature extractors. This method is fairly general and can be applied to any vision dataset, as well as different downstream tasks. In a previous tutorial, I wrote a bit of a background on the self-supervised learning arena. Time to get into your first project by running SimCLR on a small dataset with 100K unlabelled images called STL10. Code is available on Github.

Optimisation problems often have multiple conflicting objectives that can be computationally and/or financially expensive. Mono-surrogate Bayesian optimisation (BO) is a popular model-based approach for optimising such black-box functions. It combines objective values via scalarisation and builds a Gaussian process (GP) surrogate of the scalarised values. The location which maximises a cheap-to-query acquisition function is chosen as the next location to expensively evaluate. While BO is an effective strategy, the use of GPs is limiting. Their performance decreases as the problem input dimensionality increases, and their computational complexity scales cubically with the amount of data. To address these limitations, we extend previous work on BO by density-ratio estimation (BORE) to the multi-objective setting. BORE links the computation of the probability of improvement acquisition function to that of probabilistic classification. This enables the use of state-of-the-art classifiers in a BO-like framework. In this work we present MBORE: multi-objective Bayesian optimisation by density-ratio estimation, and compare it to BO across a range of synthetic and real-world benchmarks. We find that MBORE performs as well as or better than BO on a wide variety of problems, and that it outperforms BO on high-dimensional and real-world problems.