The fields of machine learning, as well as deep learning, have witnessed exponential growth in the last five years. The core machine learning models like supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning have witnessed widespread application in numerous domains. Similarly, deep learning models including artificial neural networks have also been used in a large number of projects. It is in this context that professionals are looking for machine learning certification as well as deep learning certification to enter into new and prospective disciplines.
In this article, we look into the most prominent deep learning technologies from a technical point of view.
A new hypothesis in deep learning: The lottery ticket hypothesis
The lottery ticket hypothesis gives an understanding of the working of deep neural networks. With the help of this hypothesis, it becomes possible for the researcher to identify various types of subnetworks that perform core functions and play a pivotal role in solving the problem statement. This literally means that once we are able to identify the subnetwork that performs equally well as the entire network, we can discard the original network and rely on the subnetwork for solving deep learning problems.
However, the identification of this network is a challenge in itself. This is where the lottery ticket hypothesis comes into action and helps us with random initialisation. Such a type of initialization can act as a lottery ticket for the researcher as it acts as a precursor toward the identification of the core sub-network within a larger network. Needless to mention, the larger the network is, the greater probability exists for detecting the core network or the sub-network.
Compound scaling and EfficientNet
Compound scaling involves the scaling of the entire deep learning network in a uniform manner with respect to parameters like width and depth. In simple terms, when we apply compound scaling to a deep learning network, we are actually working with Efficient Net. The case of compound scaling of a neural network involves working on three parameters. The first parameter is related to the customisation of the width of the network. The second parameter is related to the customization of the depth of the network. The third parameter is related to the manipulation of the resolution of the network.
Tabular data and TabNet
When we need to work on tabular data and arrange it in a hierarchical manner, we can use the deep learning model called TabNet. In most cases, artificial neural networks do not yield accurate results when applied to tabular data. The cases of overfitting have been witnessed and this results in the inefficiency of the overall usage of deep learning networks to data presented in tabular format. However, models like Adaboost have found prominence because they yield appropriate results while processing tabular data.
In spite of the prominence of such deep learning models, we are likely to encounter the problem of overfitting. This is where we might think of using decision trees to counter the problem. However, for more advanced mathematical problems, we need to design and develop a decision tree that is very complex. This is where TabNet comes into action. There are two main stages that are involved in the training process using TabNet. The first stage is the pre-training stage in which the model is made to predict masked values. In the second stage, the input is passed through decision-making layers as well as the TabNet encoder to yield the output.
The idea of conceiving a top-performing deep learning model with zero training has always been a subject of interest for researchers. It is in this context that the edge-popup algorithm holds particular significance. With the help of this algorithm, we are able to find the volume of information that can actually be stored in an edge. Once this information has been found out, only those edges that hold the maximum information are retained. Other edges that are non-relevant or hold little information are removed upfront. The edge pop-up algorithm has opened a new research frontier for scientists who are now trying to estimate the amount of information that the edges in an artificial neural network can actually store.
Multilayer perceptrons fall in the category of feed-forward networks. In such networks, the number of input layers is usually equal to the number of output layers. However, a large number of layers are also hidden in multi-layer perceptrons and perform various functions. Such types of networks are especially important for applications like natural language processing and image classification. Let us briefly understand the functioning of multi-layer perceptrons. The input is usually fed into the network through input layers. Between the input layers, many other layers are connected and the input information is subjected to an activation function. With the help of these functions, it becomes easy for a multi-layer perceptron to determine the dependency between input information and the target variable.
Data visualisation and self-organizing maps
It is literally impossible to manually process the volume of information that is generated across various channels. It is in this context that this large amount of information needs to be reduced in dimensionality so that it can become observable from a human perspective. Needless to mention, a finite and discrete amount of information can be processed by the human mind which can then be utilized for the power of decision making.
The transformation of this volume of information from an unorganised format to a visualised format is easier said than done. This is where self-organising maps come into action and help in reducing the dimensionality of information with the help of artificial neural networks. The applications of self-organising maps are extremely important from a business perspective since this information can serve as critical feedback for reorienting a business in the right direction.
In addition to the above mentioned technologies, we may also see the rise of new deep learning technologies in the future that help in processing large volumes of information and enabling businesses to derive critical insights in a competitive environment.