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Neural networks learn faster using ETH software

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Two researchers from the Scalable Parallel Computing Lab at the Swiss Federal Institute of Technology in Zurich (ETH) have developed a software solution to rapidly speed up the training of deep learning applications. This is important as this process is the most resource-demanding and costly step of all, ETH Zurich writes in a press release. It accounts for up to 85 percent of the training time. For example, a single training run of a sophisticated voice recognition model can cost around 10 million US dollars. The new software named NoPFS was developed by Roman Böhringer and Nikoli Dryden.


Moon's Hidden Depths Uncovered with New Algorithm

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Certain areas near the moon's poles linger perpetually in shadow, never receiving direct sunlight. Recent studies suggest these so-called permanently shadowed regions (PSRs) contain rich ice reservoirs that could reveal details about the early solar system; they could also help future visitors make fuel and other resources. But these areas are hard to photograph from satellites orbiting the moon and thus are a challenge to study. The few photons PSRs do reflect are often overwhelmed by staticlike camera noise and quantum effects. Now researchers have produced a deep-learning algorithm to cut through the interference and see these dark zones.


Deep Learning for Tabular Data

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In this review we will dive into basic explanation of the Neuronal Networks structure and potential, as well as discuss Tabular Data and the performance of the networks in this domain, reviewing the latest trials and methods in the field. Lately, Deep Learning has been heavily applied in various fields and widely searched as shown in figure 1. Figure 1: The exponential growth of published papers and Google search terms containing the term Deep Learning. Much of the deep learning hype stems from the tremendous value neural networks have shown in application areas such as computer vision [1], audio processing [2] and natural language processing -- NLP [3]. One of the most attractive attributes of deep learning is its ability to model almost any input-output relationship, and to represent complicated functions using large amounts of linear regressions. This led to the use of deep learning in a very wide array of applications. The neuronal networks are multi-layer regression models, that are interconnected, that can perform a self-learning operation in an iterative manner, while processing the input data.


Microsoft's DeepSpeed-MoE Makes Massive MoE Model Inference up to 4.5x Faster and 9x Cheaper

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A Microsoft research team proposes DeepSpeed-MoE, comprising a novel MoE architecture design and model compression technique that reduces MoE model size by up to 3.7x and a highly optimized inference system that provides 7.3x better latency and cost compared to existing MoE inference solutions.


How deep learning can improve productivity and boost business

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Deep learning, at its essence, learns from examples -- the way the human brain does. It's imitating the way humans acquire certain types of knowledge. Because deep learning processes information in a similar manner, it can be used to do things people can do – for example, learning how to drive a car or identifying a dog in a picture. Deep learning is also used to automate predictive analytics – for example, identifying trends and customer buying patterns so a company can gain more customers and keep more of them. You know those sections on retail sites that show items "frequently bought together" when you're purchasing a new screwdriver?


Deep Learning Works Like The Human Brain In Future - Prnotes

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Deep learning is a subfield or part of machine learning. Algorithms that replicate or inspire the human brain, consisting of algorithms designed to mimic the structure and operation of the human mind, are called artificial neural networks. It is an artificial intelligence function that mimics the human brain to process data and generates patterns used in decision-making. Unsupervised training is possible on any given data set. Deep learning is also called deep neural learning or deep neural networks.


GIST Researchers Develop Terrain-Aware AI for Predicting Battle Outcomes in StarCraft 2

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As the need for more sophisticated artificial intelligences (AIs) grows, the challenges that they must face along the way have to evolve accordingly. Real-time strategy (RTS) video games, unlike turn-based board games such as chess, can serve as a vast playground for pushing the limits of AI. In particular, StarCraft II (SC2), one of the world's most popular and skill-demanding RTS games, has already been the object of a few groundbreaking AI-related studies. In SC2 matches, each player has to build up and command an army of varied units to defeat their opponent using wit and grit. While AI-based systems can excel at many aspects of the game, improving their decision-making regarding when their units should be sent to or relocated during a battle is remarkably difficult.


Scientific Documents Similarity Search With Deep Learning Using Transformers (SciBERT)

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Let have a look at some random articles. Here we limit the printing to the first hundred words, because some of them are very long. This step aims to vectorize the articles' abstract text so that we can perform the similarity analysis. Since we are dealing with the scientific documents, we will use SciBERT, which is a pre-trained language model for Scientific text data. You can find more information about it on Semantic Scholar.


Artificial Intelligence (AI) vs. Machine Learning (ML) vs. Deep Learning

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It is startling how technology has altered our lives in recent years. We mostly don't notice how much we rely on the use of artificial intelligence tools. Nevertheless, we rely on it in many spheres of our lives. Human existence is increasingly bound to technologies, that seem to be able to exercise judgments and operate independently. The question of whether computers can actually think still remains open.


TinyML is bringing neural networks to small microcontrollers

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This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Deep learning models owe their initial success to large servers with large amounts of memory and clusters of GPUs. The promises of deep learning gave rise to an entire industry of cloud computing services for deep neural networks. Consequently, very large neural networks running on virtually unlimited cloud resources became very popular, especially among wealthy tech companies that can foot the bill. But at the same time, recent years have also seen a reverse trend, a concerted effort to create machine learning models for edge devices.