Accurate, although in most cases, the art is not in choosing the algorithm (often a regular feed-forward, covolutional or recurrent neural network) but the hyperparameters (regularization, number of layers, units, etc.) and sometimes the way the data is fed (augment the dataset with distortions? sample negative examples randomly? etc.)
- Tensorflow is the best tool to make the grunt work necessary to calibrate the parameters of fitting algorithms.
- Models of fitting algorithms are human proposed and where the ML 'art' is. Seems to be kind of reverse engineering.