Oracle JET works with any kind of REST service, such service could be the one coming from TensorFlow (read more in my previous post – TensorFlow Linear Regression Model Access with Custom REST API using Flask). I have implemented linear regression model with gradient descent optimizer in TensorFlow (will describe this in more detail in my next post – machine learning is all about mathematics) and consuming it in JET UI:
There is option to define training steps (or data points) and learning rate. As outcome we get W and b values for linear equation y = Wx + b. After training is executed (so called machine learning process) – W and b parameters are identified, this allows to predict y value for any x. More about this in my next post, today will focus on JET. Read the complete article here.
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