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13-12-2020 11:29 GMT | #cagd 4 kaggle notebooks A large number of kaggle notebooks are getting updated recently with notes & new tutorials covering some advanced ML tasks. This is the list of current notebooks which have been found. Reverse Engineering Neural Networks [This is the original blog post on which this page is based.] | Code available here: In this blog post I wanted to take a step back from looking at the code of Deep Learning models and look at the data, what I learned and how I used the same knowledge to reverse engineer an LSTM. How to beat the AI system with a finite bankroll using XM | Implementing a probabilistic model for trading on your own. This post by Jeremy Jugane of Finetrica looks at the rise in the use of AI in the financial industry, how data providers are adapting their solutions to it and how a certain application could be treated in order to beat the AI system. Big data for predictive maintenance | This is a very insightful look at how Big Data and predictive maintenance can be used together. How businesses create software with artificial intelligence | Some very interesting points, for example, the "distributed world with a finite amount of energy", the importance of hiring AI developers and the concept of "human-centred AI". Gathering a Data Lake of Big Data | This interesting read looks at the current concept of a Data Lake, where both structured data such as SQL tables and unstructured data such as YouTube videos are stored and later processed. This is a very useful concept for data scientists looking at the future of Big Data. How AI is transforming the financial services industry | Some very interesting points, for example, "the vast amount of data to be processed", "big data", "artificial intelligence" & "fintech". Probabilistic Logic Networks | This paper looks at a new class of logical formalism called "probabilistic logic networks" and how it can help the development of AI. In the last couple of years, the concept of data lakes has emerged. This is an interesting way of storing and indexing data, which is not only useful for data scientists, but also for other IT departments that need access to some of their data. As data lakes are becoming more and more common, we thought we would look at

 

 


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