I am doing a work that is based on analyzing different Python libraries for Machine Learning.
I chose to analyze Scikit-Learn, Keras, Tensorflow and Pytorch for being the most known ones. The idea was to train different models, both supervised and unsupervised learning, as well as classification and regression. Using different algorithms of each type in each of the libraries and analyzing the difficulties/facilities they present as well as the performance.
My problem arises when trying to perform the same as in sklearn in Tensorflow, since for the latter I find a lot of information for deep learning (neural networks), but not for machine learning algorithms (decision trees, Random Forests, SVM, Linear/Logistic Regression, K-NN, KMeans, Naive-Bayes, etc...).
Is it possible to apply these ML algorithms in tensorflow, or is it only oriented for Deep Learning?