Hi, this is my first time posting in this group so I apologize in advance if this is the wrong place.
I recently picked up the Deep Learning with Python book by Chollet as recommended by Google when first starting to learn Tensorflow. However, I thought a good place to start would be to learn how to code basic ML models like Linear Regression, Support Vector Machines, Random Forests, and basic methods like nested cross validation, preprocessing, grid search. These are all methods I know how to do in sklearn, but want to know in Tensorflow. What book/place would do a good job of explaining the code and intuition behind those models?
I am not looking for what the models mean or how they work, more so how to write them properly in Tensorflow. I took a look at Tensorflow’s tutorial on Linear Regression and it made little sense creating a keras.Sequential model with a Dense layer. Seems similar to a Neural Network. I figure that confusion is because I do not know the reason behind the code.
Thank you in advance.
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