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Deep Learning is a type of Neural Network Algorithm that takes metadata as an input and process the data through a number of layers of the non-linear transformation of the input data to compute the output.
This algorithm has a unique feature i.e. automatic feature extraction. This means that this algorithm automatically grasps the relevant features required for the solution of the problem.
This reduces the burden on the programmer to select the features explicitly. This can be used to solve supervised, unsupervised or semi-supervised type of problems.
In Deep Learning Neural Network, each hidden layer is responsible for training the unique set of features based on the output of the previous layer. As the number of hidden layers increases, the complexity and abstraction of data also increase.
It forms a hierarchy from low-level features to high-level features. With this, it becomes possible that Deep Learning Algorithm can be used to solve higher complex problems consisting of a large number of non-linear transformational layers.
Machine Learning is a set of the technique used for the processing of large data by developing algorithms and set of rules to deliver the required results to the user. It is the technique used for developing automated machines on the basis of execution of algorithms and set of defined rules.
In Machine Learning data is fed and set of rules are executed by the algorithm. Therefore, techniques of Machine Learning can be categorized as instructions that are executed and learned automatically to produce optimum results.
It is performed without any human interference. It automatically turns the data into patterns and goes deep inside the system for the detection of production problem automatically.
The traditional neural network consists of at most 2 layers and this type of structure of the neural network is not suitable for the computation of larger networks. Therefore, a neural network having more than 10 or even 100 layers are introduced.
This type of structure is meant for Deep Learning. In this, a stack of the layer of neurons is developed. The lowest layer in the stack is responsible for the collection of raw data such as images, videos, text, etc.
Each neuron of the lowest layer will store the information and pass the information further to the next layer of neurons and so on. As the information flows within the neurons of layers hidden information of the data is extracted.
So, we can conclude that as the data moves from lowest layer to highest layer (moving deep inside the neural network) more abstracted information is collected.
Deep Learning works on the basis of the architecture of the network and the optimum procedure used by the architecture. The type of network followed is known as a Directed graph. The graph is designed in such a way that each hidden layer is connected with every hidden node.
So, combination and recombination of outputs from all units of hidden layer are performed in the context of the combination of their activation functions. This procedure is known as Non-Linear Transformation. After that optimum procedure is applied to the network to produce optimum weights for each unit of a layer.
This is the whole routine for the flow of information inside the hidden layers to produce required target output.
Too many hidden layers present in the algorithm is not feasible. This is because the neural network is trained with the simple gradient descent procedure. If a huge number of hidden layers are involved in the algorithm, then this gradient descent will be reduced that further affects the output.
Neural Network is a network that can use any network such as feedforward or recurrent network having 1 or 2 hidden layers. But, when the number of hidden layers increases i.e. more than 2 than that is known as Deep Learning Neural Network.
Neural Network is less complex and requires more information about features for performing feature selection and feature engineering method. On the other hand, Deep Learning Neural Network does not require any information about features rather they perform optimum model tuning and model selection on their own.
In today’s generation usage of smartphones and chips have increased drastically. Therefore, more and more images, text, videos, and audios are created day by day. But, as we know that a single layer neural network can compute complex function.
On the contrary, for the computation of complex features deep learning is needed. This is because deep nets within the deep learning method have the ability to develop a complex hierarchy of concepts.
Another point is that when unsupervised data is collected and machine learning is executed on it, manually labeling of data has to be performed by the human being. This process is time-consuming and expensive. Therefore, to overcome this problem deep learning is introduced as they have the ability to identify the particular data.
There are various methods that are introduced for the analysis of log file such as pattern recognition methods like K-N Algorithm, Support Vector Machine, Naive Bayes Algorithm etc. due to the presence of a large amount of log data, these traditional methods are not feasible to produce efficient results.
Deep Learning Neural Network shows excellent performance in analyzing the log data. It consists of excellent computational power and automatically extracts the features required for the solution of the problem. Deep learning is a subpart of Artificial Intelligence. It is a deeply layered learning process of the sensor areas in the brain.
Different techniques of Deep Learning are described below:
Deep learning neural network plays an important role in knowledge discovery, knowledge application, and last but least knowledge-based prediction.
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