Research often includes the confrontation with various complex dynamics and abstractions that are not exactly well-defined and easily accessible to our understanding becomes difficult to manage. This is where a deep neural network comes in handy. It can learn and show better performance than regular machine learning models. Moreover, they help in making new discoveries in different fields.
However, the usage of DNN is infamously known as Black Box, owing to its lack of transparency. The lack of insight is what derives this term.
Nonetheless, to fully understand the concept of Black Box, one needs to have a better idea of what a deep neural network is.
What is a Deep Neural Network?
A neural network is a computer architecture in which processors are interconnected in a way that replicates the connections between neurons in a human brain. This way, the neural network, both in the brain and computer, is able to learn various things by trial and error.
A deep neural network or DNN is an artificial neural network or ANN, which has several layers between the layer of input and output.
Deep neural networks (DNNs) are capable of automatically studying abstract data that can be completely original and fresh. DNNs perform superiorly compared to classical machine learning models. Therefore, they are more promising tools to make new discoveries in fields like pharmacology, psychiatry and space research. The scope of applications of DNNs is increasing with an increase in programming and research.
How Does a Deep Neural Network Behave?
Deep neural networks belong to a broader category of neural networks (NNs). Neural networks, also known as artificial neural networks and inspired by a human brain.
A biological neuron takes input from different neurons, forms the potential for action and then they output signals to other neurons through synapses. These artificial neurons are connected analogously. Synaptic strengths are chosen by weight. So, the principle is - the higher the weight, the stronger the synaptic connection. The action potential is simulated by a nonlinear activation function, which changes after the input value surpasses a certain threshold value.
Emergence of Big Data Brought Forward the Black Box Problem
The rise of Big Data and new developments in machine learning might provide insights into some of the challenges. Deep neural networks (DNNs), a specific type of ML model, can help understand these networks. Our biological brains have inspired DNN models. The neural network uses artificial neurons as units and wires a large number of units together in particular ways forming various types of architectures.
Common DNN Architectures
Neural networks typically consist of layers of artificial neurons. They take signals from their equivalent counterparts from the preceding layer. Then, after the next transformation that we mentioned earlier, it sends the output to the subsequent layer to another equivalent counterpart in that layer. Normally, there is zero connection between neurons in the same layer.
However, in real-world applications, a number of neurons input the data, link to a single output cell and apply a logistic activation function at the output cell, and send the output. This gives rise to DNN architecture.
- Feedforward Neural Network (FNN)
A feedforward neural network is an ANN in which connections between the nodes do not form a cycle. A layer is different from its successor or the recurrent neural networks. It can be used for a variety of things like data compression, pattern recognition and voice recognition.
- Convolutional Neural Network (CNN)
A convolutional neural network (CNN) is an artificial neural network which is used in image recognition and processing that is specifically designed to process pixel data. CNN identifies several objects on images which makes it useful in medicine or MRI diagnostics. CNN is also used in agriculture. They are feedforward neural networks which employ filters and are used for pooling layers.
- Recurrent Neural Network (RNN)
A recurrent neural network (RNN) is a neural network in which the output from the preceding step is fed as input to the present step. Its real-world applications are language modelling and text generation, call centre analysis and video tagging.
- Learning Model Parameters
In simple models like linear regression, the estimation of model parameters can be done with a closed-form and fixed solution. For ML models which have some degree of complexity, one would have to depend upon numerical approximations for obtaining estimates. This can be learned with gradients during model training. Gradients calculated for all parameters of DNNs use a method called back propagation, where a model output would change by a small change in the input. This method of back propagation makes it useful for model interpretation.
To sum up, the concept of Black Box, even though it is widely known, the interpretation of it is still somewhat half-hearted. Hopefully, in the coming years, researchers will be able to shed some light on this topic.
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