Community detection and hierarchy extraction are usually thought of as separate inference tasks on networks. Considering only one of the two when studying real-world data can be an oversimplification. In this work, we present a generative model based on an interplay between community and hierarchical structures. It assumes that each node has a preference in the interaction mechanism and nodes with the same preference are more likely to interact, while heterogeneous interactions are still allowed. The algorithmic implementation is efficient, as it exploits the sparsity of network datasets. We demonstrate our method on synthetic and real-world data and compare performance with two standard approaches for community detection and ranking extraction. We find that the algorithm accurately retrieves each node's preference in different scenarios and we show that it can distinguish small subsets of nodes that behave differently than the majority. As a consequence, the model can recognise whether a network has an overall preferred interaction mechanism. This is relevant in situations where there is no clear "a priori" information about what structure explains the observed network datasets well. Our model allows practitioners to learn this automatically from the data.

Current state-of-the-art deep learning based face recognition (FR) models require a large number of face identities for central training. However, due to the growing privacy awareness, it is prohibited to access the face images on user devices to continually improve face recognition models. Federated Learning (FL) is a technique to address the privacy issue, which can collaboratively optimize the model without sharing the data between clients. In this work, we propose a FL based framework called FedFR to improve the generic face representation in a privacy-aware manner. Besides, the framework jointly optimizes personalized models for the corresponding clients via the proposed Decoupled Feature Customization module. The client-specific personalized model can serve the need of optimized face recognition experience for registered identities at the local device. To the best of our knowledge, we are the first to explore the personalized face recognition in FL setup. The proposed framework is validated to be superior to previous approaches on several generic and personalized face recognition benchmarks with diverse FL scenarios.

This is PART 2 of our 3 PART series on the machine learning lifecycle platform MLflow. In PART 1, we've had a look at: In this guide, we are going to have a look at MLflow Models. With Models, we can package machine learning/deep learning models for deployment in a wide array of environments. Note that this guide assumes that you've read PART 1. So make sure to check out the first article in the series before reading on!

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After discussing the design phase, the reader will familiarize themselves with best practices on how to write maintainable deep learning code such as OOP, unit testing, and debugging. Chapter 5 is all about building efficient data pipelines, while Chapter 6 deals with model training in the cloud as well as various distributed training techniques.

The British technology sector enjoyed its best year of investment yet, attracting £29.4 billion in 2021. So, why hasn't the UK's AI and tech scene made it to the same global superpower status that we see from China, Russia, and the US? We don't need to wait for the results of the government's recent National AI Strategy, to know that deeper change is needed to really propel the industry forward. To create an AI scene that can compete on the world stage, education, ambition, and innovation must be combined and accelerated. Here are three tactics I believe could propel the UK's AI industry forward this year: To capitalise on the promise of AI, businesses and universities must build stronger links between one another, to drive innovation that can more quickly and effectively reach the market.

The world of data science is still new as compared to other software-related fields, and it doesn't have a gold standard on what skills you need to acquire to be called a professional data scientist. This is where DataCamp certification comes in to access your knowledge and skills. Just like in the world of computer networks, the Cisco certification is a gold standard. Similarly, DataCamp is accessing an individual's skills by conducting various challenges. During the Certificate Challenge, I was a professional data scientist working with various companies on various projects.

Humans are great at classifying noises. We can hear a chirp and surmise that it belongs to a bird, we can hear an abstract noise and classify it as speech with a particular meaning and definition. This relationship between humans and audio classification forms the basis of speech and human communication as a whole. Translating this incredible ability to computers on the other hand can be a difficult challenge, to say the least. Whilst we can naturally decompose signals, how do we teach computers to do this, and how do we show what parts of the signal matter and what parts of the signal are irrelevant or noisy?