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Compute Trends Across Three Eras of Machine Learning

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Compute, data, and algorithmic advances are the three fundamental factors that guide the progress of modern Machine Learning (ML). In this paper we study trends in the most readily quantified factor - compute. We show that before 2010 training compute grew in line with Moore's law, doubling roughly every 20 months. Since the advent of Deep Learning in the early 2010s, the scaling of training compute has accelerated, doubling approximately every 6 months. In late 2015, a new trend emerged as firms developed large-scale ML models with 10 to 100-fold larger requirements in training compute. Based on these observations we split the history of compute in ML into three eras: the Pre Deep Learning Era, the Deep Learning Era and the Large-Scale Era. Overall, our work highlights the fast-growing compute requirements for training advanced ML systems.


The Difference between Machine Learning and Deep Learning

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Get a very good understanding of machine learning and deep learning. Also learn to differentiate between the two terms rather than using them interchangeably. There has been a growth in the search terms such as "Machine Learning" and "Artificial Intelligence" where people are trying to possibly learn and grasp these sophisticated terms and understand their potential uses.


What is a Neural Network?

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Here we cover topics in deep learning and neural networks in small chunks. These nodes link together to form a knowledge base on deep learning, not unlike the networks themselves. An artificial neural network is an algorithm that uses data and mathematical transformations to build a model that performs regressions or classifications based on new, similar data. Deep learning networks are made of consecutive layers of data transforming nodes. Strengths of neural networks include finding patterns in large datasets with limited prior knowledge about the trends.


Deep Learning Engineer

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As a deep learning engineer at Arbrea Labs, you will be working at the interface between research and production code, where a wide array of different tasks arise in this unique context. As one of the key employees, you will have a strong influence on how our product will be shaped, giving you, altogether with the team, the opportunity to grow into world leaders in Medical AR/VR technology. You will work with friendly, passionate, and easy-going team members, be very willing to offer guidance, and have a beer (or two) after work or regular team events. The working environment is quite flexible, with offices in a Technology hub, attractive salaries and we offer employee participation plans. Arbrea Labs is an ETH spin-off from the Computer Graphics Lab, ETH Zurich.


DeepMind Scholarship

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The DeepMind Scholarships are positive action initiatives to help UCL ensure that it can attract and support students from all sections of the community, particularly groups that are under-represented in post-graduate studies.