Three great breakthroughs in deep learning models that we would witness in 2021
In the year 2021, there has been an exponential rise in research related to deep learning technologies. Deep learning has become one of the fastest-growing technological domains in both academic and industrial sectors. As such, the field of deep learning has sparked off great interest even in the common masses. We have seen renewed interest shown by professionals towards deep learning certification as this acts as a virtual passport for entry into lucrative industrial jobs. It needs to be noted at this point in time that deep learning has seen several advancements in the past and this trend is continuing in the present time as well. Let’s look at some of the advances we would witness in deep learning technologies in the year 2021.
If we are given a large number of shallow neural networks, we can apply gradient boosting to these networks. In this way, GrowNet helps in the application of algorithms like classification and regression. It also helps in ranking events in a hierarchical manner. In addition to this, we may also use the above technique to correct the mistakes of the previous models. Usually, there are k models in the above ensemble. We take a large number of original features and also use the output of other models as training data sets. The aim is to produce very accurate results by not only rectifying the mistakes of other models but also aggregating their outputs to yield final results.
One of the most popular models for handling tabular data is called TabNet. This model works in a similar way to the decision trees and is capable of representing information in a hierarchical manner. The hierarchical representation of data sets is the main reason behind its usage in various real-world applications. This model also handles complex data sets and also avoids overfitting to some extent.
The problem that we encountered in decision tree models was the splitting of the sample space in perpendicular planes. Moreover, the problem of overfitting was a drawback of these models and this made us switch over to Tabnet. Furthermore, the boundaries of separation that this model provides between various data sets are discrete as well as accurate.
One of the most effective models that are used in image recognition is the EfficientNet. In the past, we have relied on convolutional neural networks for performing operations like increase in depth, width as well as image size. However, the great accuracy of EfficientNet has been one of the main reasons for switching to this technique. The operations that can be performed with the help of EfficientNet include baseline operations, width scaling, depth scaling, resolution scaling, and compound scaling.
The way ahead
In the present time, research is being carried out for the development of randomly initialized networks where the training requirements are relatively lesser. This may act as a precursor to the development of future models that can cater to the requirements of both structured and unstructured data sets