This very much. Understanding the underlying concepts is crucial. Many people use out-of-the-box tools just plainly wrong, because they didn't bother with funda

This very much. Understanding the underlying concepts is crucial. Many people use out-of-the-box tools just plainly wrong, because they didn't bother with fundamentals. I know a guy who reinvented Naive Bayes and Laplace smoothing. Kudos for him, but he wasted about 3-4 months of company's time on it. That said, I would start with Python based frameworks: - Caffe is more for production, and Theano and family for experimentation. - You can play with Theano or Tensorflow using Keras which abstracts them - Python scientific stack makes it easier to visualize the results later