When and When Not to Use Deep Learning

Despite its increasing accessibility, deep learning in practice still remains a complicated and expensive endeavor. For one thing, due to their inherent complexity, the large number of layers and the massive amounts of data required, deep learning models are very slow to train and require a lot of computational power, which makes them very time- and resource-intensive. Graphics Processing Units, or GPUs, have practically become a requirement nowadays to execute deep learning algorithms. GPUs are very expensive yet without them training deep networks to high performance would not be practically feasible.