Deep Learning is a subset of machine learning which has shown huge potential on various tasks such as computer vision, classification, regression etc.

Deep Learning models’ computation ability mostly comes from its architecture and large amount of data they can store and process to find any kind of relation between data and targets which in most cases were not feasible using less complex Machine Learning algorithms.

Despite the great capabilities, DL models yet need to overcome a few challenges. Here we will briefly discuss important ones.

Heuristic models

Designing a deep learning model requires big deal of experience, domain knowledge and dexterity as there is no obvious pattern for building up a model optimized for our task. Lack of a comprehensive paradigm for creating a model to perform best in our domain, remains as the most challenging issue with deep learning algorithms application.

Recently some advanced methods have been introduced that solves this problem to some extent. Among the most popular and successful ones are GrowNet and TabNet.

These methods enable us to perform interpretable and delicate hyper tuning and also build up models by stacking shallow deep neural networks as “weak learners”.

Limited data

As the models get larger and stronger, they need more data to reach their optimum weights and achieve the best Accuracy that they potentially can. In a lot of cases, we deal with limited data sources that we do not have enough data to appropriately train our model. There are times that abundant data is available, but it is not labeled, or in other words it is not clear which class the data belongs to. When it comes to machine learning algorithms, it is important for a model to have a label for each data to proceed the training process, otherwise we need to use Unsupervised Learning Algorithms which introduces extra complexity to the model.

To tackle aforementioned problems, some solutions have been suggested. Among them is Transferred Learning.

Transferred Learning is an approach that pre-trains models on data from outside the training distribution. This will let the model make an acceptable prediction on small amounts of data, also it is versatile and has reasonable performance on unseen data.

Incorporating Logic

Incorporating some sort of rule-based knowledge, so that logical procedures can be implemented and sequential reasoning used to formalize knowledge.

While these cases can be covered in code, Machine Learning algorithms don’t usually incorporate sets or rules into their knowledge. Sets of pre-defined rules could assist Deep Learning systems in their reasoning.

Computation load

As already mentioned, deep learning models’ functionality is dependent on neurons which are not more that series of weights and numbers. The process of calculating these weights with back propagation and other techniques, can sometimes be really time taking and require large computational resources.

Long way to go

Deep neural networks in spite of their seemingly formidable power, yet have a long way to go to be comparable with human brains ability. But with recent advances and promising results, scientists anticipate a bright future for deep neural networks.