Deep neural networks have shown the ability to extract universal feature representations from data such as images and text that have been useful for a variety of learning tasks. However, the fruits of representation learning have yet to be fully-realized in federated settings. Although data in federated settings is often non-i.i.d. across clients, the success of centralized deep learning suggests that data often shares a global feature representation, while the statistical heterogeneity across clients or tasks is concentrated in the labels. Based on this intuition, we propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. We prove that this method obtains linear convergence to the ground-truth representation with near-optimal sample complexity in a linear setting, demonstrating that it can efficiently reduce the problem dimension for each client. This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions, for example in meta-learning and multi-task learning. Further, extensive experimental results show the empirical improvement of our method over alternative personalized federated learning approaches in federated environments with heterogeneous data.

In many areas of the observational and experimental sciences data is scarce. Data observation in high-energy astrophysics is disrupted by celestial occlusions and limited telescope time while data derived from laboratory experiments in synthetic chemistry and materials science is time and cost-intensive to collect. On the other hand, knowledge about the data-generation mechanism is often available in the sciences, such as the measurement error of a piece of laboratory apparatus. Both characteristics, small data and knowledge of the underlying physics, make Gaussian processes (GPs) ideal candidates for fitting such datasets. GPs can make predictions with consideration of uncertainty, for example in the virtual screening of molecules and materials, and can also make inferences about incomplete data such as the latent emission signature from a black hole accretion disc. Furthermore, GPs are currently the workhorse model for Bayesian optimisation, a methodology foreseen to be a guide for laboratory experiments in scientific discovery campaigns. The first contribution of this thesis is to use GP modelling to reason about the latent emission signature from the Seyfert galaxy Markarian 335, and by extension, to reason about the applicability of various theoretical models of black hole accretion discs. The second contribution is to extend the GP framework to molecular and chemical reaction representations and to provide an open-source software library to enable the framework to be used by scientists. The third contribution is to leverage GPs to discover novel and performant photoswitch molecules. The fourth contribution is to introduce a Bayesian optimisation scheme capable of modelling aleatoric uncertainty to facilitate the identification of material compositions that possess intrinsic robustness to large scale fabrication processes.

Machine Learning is a rapidly growing field with immense potential to revolutionize various industries. Learning machine learning can be complicated, and we often need help figuring out where to start. With the increasing availability of free resources, we end up spending a lot of time figuring out the best resources to hone our skills. With this in mind, we have compiled a list of the top 15 machine-learning channels that offers valuable insights, tips, and tutorials. Whether you are a beginner looking to gain a solid understanding of the foundations or an expert seeking to deepen your knowledge and stay up to date with the latest trends, these channels will offer a wealth of information from some of the top minds and biggest brands in the community.

The graph represents a network of 1,419 Twitter users whose recent tweets contained "iot bigdata", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 3/14/2023 5:00:36 PM. The network was obtained from Twitter on Wednesday, 15 March 2023 at 11:43 UTC. The tweets in the network were tweeted over the 2136-day, 23-hour, 8-minute period from Monday, 08 May 2017 at 00:51 UTC to Tuesday, 14 March 2023 at 23:59 UTC. There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

GPT Summary: The rapid advancements in artificial intelligence (AI) have brought humanity to a critical juncture, similar to the Renaissance of the 14th-17th centuries. AI is experiencing a functional rebirth, with machines surpassing human performance in various cognitive tasks. These developments raise philosophical questions about the nature of human intelligence and our roles in a world where AI is omnipresent. Striking the right balance between innovation and regulation is crucial, as ethical concerns need addressing. By exploring AI's function and philosophical aspects, we can harness its power to enhance our lives, create new opportunities, and unlock the next renaissance in human-machine collaboration.

Artificial intelligence is emerging as one of the most powerful agents of change in higher education, presenting the sector with unprecedented academic, ethical and legal challenges. Through its algorithmic ability to adapt, self-correct and learn, AI is pushing the boundaries of human intelligence, making the future of higher education inextricably intertwined with AI. To disentangle the intertwined relationship between AI and higher education, I will briefly discuss the opportunities and the challenges of AI, review some of the emerging applications of AI in higher education, and offer some recommendations for the way forward. As an umbrella term that includes machine learning, deep learning and natural language processing, AI relies on extensive computing power and massive amounts of data processed by algorithms. As it continues to seep into the fabric of our society, AI is being used to solve problems in cybersecurity, health care, agriculture, climate change, manufacturing, banking and fraud detection, among other areas.

Let us start with an easy example, say you are teaching a kid to differentiate dogs from cats. How would you do it? You may show him/her a dog and say "here is a dog" and when you encounter a cat you would point it out as a cat. When you show the kid enough dogs and cats, he may learn to differentiate between them. If he is trained well, he may be able to recognize different breeds of dogs which he hasn't even seen. Similarly, in Supervised Learning, we have two sets of variables.

On behalf of Tesco Bengaluru, we must caution all job seekers and educational institutions that Tesco Bengaluru does not authorise any third parties to release employment offers or conduct recruitment drives via a third party. Hence, beware of inauthentic and fraudulent job offers or recruitment drives from any individuals or websites purporting to represent Tesco. Further, Tesco Bengaluru does not charge any fee or other emoluments for any reason (including without limitation, visa fees) or seek compensation from educational institutions to participate in recruitment events. Accordingly, please check the authenticity of any such offers before acting on them and where acted upon, you do so at your own risk. Tesco Bengaluru shall neither be responsible for honouring or making good the promises made by fraudulent third parties, nor for any monetary or any other loss incurred by the aggrieved individual or educational institution.

Machine learning and the world of artificial intelligence (AI) are no longer science fiction. Get started with the new breed of software that is able to learn without being explicitly programmed, machine learning can access, analyze, and find patterns in Big Data in a way that is beyond human capabilities. The business advantages are huge, and the market is expected to be worth $47 billion and more by 2020. In this course, you will implement your own custom algorithm on top of SAP's HANA Database, which is an In-Memory database capable of Performing huge calculation over a large set of Data. We are going to use Native SQL to write the algorithm of Naive Bayes.

In this paper, we consider distributed optimization problems where $n$ agents, each possessing a local cost function, collaboratively minimize the average of the local cost functions over a connected network. To solve the problem, we propose a distributed random reshuffling (D-RR) algorithm that invokes the random reshuffling (RR) update in each agent. We show that D-RR inherits favorable characteristics of RR for both smooth strongly convex and smooth nonconvex objective functions. In particular, for smooth strongly convex objective functions, D-RR achieves $\mathcal{O}(1/T^2)$ rate of convergence (where $T$ counts epoch number) in terms of the squared distance between the iterate and the global minimizer. When the objective function is assumed to be smooth nonconvex, we show that D-RR drives the squared norm of gradient to $0$ at a rate of $\mathcal{O}(1/T^{2/3})$. These convergence results match those of centralized RR (up to constant factors) and outperform the distributed stochastic gradient descent (DSGD) algorithm if we run a relatively large number of epochs. Finally, we conduct a set of numerical experiments to illustrate the efficiency of the proposed D-RR method on both strongly convex and nonconvex distributed optimization problems.