Watson Studio

58 Entries
 
 
one year ago


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one year ago

10 reasons why enterprises invest in AI platforms

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one year ago


This is the first article of the multi-part series on self learning AI-Agents or to call it more precisely — Deep Reinforcement Learning. The aim of the series isn’t just to give you an intuition on these topics.

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one year ago


Understanding a statistical phenomenon and the importance of asking why

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one year ago


Statistics can be a powerful tool when performing the art of Data Science (DS). From a high-level view, statistics is the use of mathematics to perform technical analysis of data. A basic visualisation such as a bar chart might give you some high-level information, but with statistics we get to operate on the data in a much more information-driven and targeted way. The math involved helps us form concrete conclusions about our data rather than just guesstimating.

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one year ago


Using high-level frameworks like Keras, TensorFlow or PyTorch allows us to build very complex models quickly. However, it is worth taking the time to look inside and understand underlying concepts. 

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one year ago


NumPy is a powerful Python library that can greatly increase the speed and efficiency of processing large data sets. Several data science and machine learning frameworks work in conjunction with or are built on top of NumPy.

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one year ago


Musicologists are beginning to uncover statistical patterns that govern how trends in musical composition have spread.

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one year ago


As Machine Learning (ML) is becoming an important part of every industry, the demand for Machine Learning Engineers (MLE) has grown dramatically. MLEs combine machine learning skills with software engineering knowhow to find high-performing models for a given application and handle the implementation challenges that come up — from building out training infrastructure to preparing models for deployment. New online resources have sprouted in parallel to train engineers to build ML models and solve the various software challenges encountered. However, one of the most common hurdles with new ML teams is maintaining the same level of forward progress that engineers are accustomed to with traditional software engineering.

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