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This algorithm is based on a self-learning method, where the agent uses information obtained during interaction with the environment to generate "intrinsic" rewards and update its strategy. The algorithm is based on the use of several agent models that interact with the environment and generate various predictions. If the models disagree, it is considered an "interesting" event and the agent is incentivized to explore that space of the environment. In this way, the algorithm incentivizes the agent ...
The Treasury Department kicked off this week's series of announcements of the results of its long-term securities auctions on Monday, revealing this month's auction of $56 billion worth of three-year notes attracted below average demand. The three-year note auction drew a high yield of 4.256 percent and a bid-to-cover ratio of 2.60. more...
Regression is a task of predicting a real value from an unlabeled example. A well-known example of regression is estimating the value of a diamond based on such characteristics as size, weight, color, clarity, etc. The so-called regression metrics are used to assess the accuracy of regression model predictions. Despite similar algorithms, regression metrics are semantically different from similar loss functions. more...
With the advancement of machine learning and artificial intelligence technologies, there is a growing need to optimize processes for working with models. The efficiency of model operation directly depends on the data formats used to represent them. In recent years, several new data types have emerged, specifically designed for working with deep learning models. In this article, we will focus on two such new data formats - float16 and float8, which are beginning to be actively used ...
LDA is a supervised generalization machine learning algorithm that aims to find a linear combination of features that best separates the classes in a dataset. Just like the Principal Component Analysis(PCA), it is a dimension reduction algorithm, These algorithms are a common choice for dimensionality reduction, in this article we are going to compare them and observe in what situation each algorithm works best. We already discussed the PCA in the prior articles of this ...
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