rasbt/python-machine-learning-book

TensorFlow is more of a low-level library; basically, we can think of TensorFlow as the Lego bricks (similar to NumPy and SciPy) that we can use to implement machine learning algorithms whereas scikit-learn comes with off-the-shelf algorithms, e.g., algorithms for classification such as SVMs, Random Forests, Logistic Regression, and many, many more. TensorFlow really shines if we want to implement deep learning algorithms, since it allows us to take advantage of GPUs for more efficient training. To get a better idea of how these two libraries differ, let's fit a softmax regression model on the Iris dataset via scikit-learn: Now, if we want to fit a Softmax regression model via TensorFlow, however, we have to "build" the algorithm first. But it really sounds more complicated than it really is. TensorFlow comes with many "convenience" functions and utilities, for example, if we want to use a gradient descent optimization approach, the core or our implementation could look like this: