When and When Not to Use Deep Learning
Deep learning is all the rage today, as major breakthroughs in the field of artificial neural networks in the past few years have driven companies across industries to implement deep learning solutions as part of their AI strategy. From chatbots in customer service to image and object recognition in retail, and much more, deep learning has unlocked a myriad of sophisticated new AI applications. But is it always the right way to go?
The outstanding performance of deep learning algorithms with complex tasks requiring huge amounts of data, combined with the increasing availability of pre-trained models on publicly available data, have made deep learning particularly appealing to many organizations in the past few years. However, this doesn’t mean that deep learning is the answer to all machine learning (ML) related problems.
But how to tell when deep learning is necessary, and when it isn’t? Obviously, each use case is very individual and will depend on your specific business objectives, AI maturity, timeline, data, and resources, among other things. Below are some of the general considerations to take into account before deciding whether or not to use deep learning to solve a given problem.
One of the main advantages of deep learning lies in being able to solve complex problems that require discovering hidden patterns in the data and/or a deep understanding of intricate relationships between a large number of interdependent variables. Deep learning algorithms are able to learn hidden patterns from the data by themselves, combine them together, and build much more efficient decision rules.
Deep learning really shines when it comes to complex tasks, which often require dealing with lots of unstructured data, such as image classification, natural language processing, or speech recognition, among others. However, for simpler tasks that involve more straightforward feature engineering and don’t require processing unstructured data, classical machine learning may be a better option.
While deep learning reduces the human effort of feature engineering, as this is automatically done by the machine, it also increases the difficulty for humans to understand and interpret the model. In fact, model interpretability is one of deep learning’s biggest challenges.
When evaluating any machine learning model, there is usually a tradeoff to be made between accuracy and interpretability. Deep networks have achieved accuracies that are far beyond that of classical ML methods in many domains, but as they model very complex situations with high non-linearity and interactions between inputs, they are nearly impossible to interpret.
On the contrary, due to the direct feature engineering involved in classical ML, these algorithms are quite easy to interpret and understand.
