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What is Deep Learning?
Deep Learning is a Neural Network Algorithm that takes metadata as an input and processes the data through some layers of the input data's nonlinear transformation to compute the output. This algorithm has a unique feature, i.e., automatic feature extraction. It means that this algorithm automatically grasps the relevant features required for the solution of the problem. Deep Learning reduces the burden on the programmer to select the features explicitly. It is beneficial to solve supervised, unsupervised, or semisupervised types of challenges.Get the predictions for future values from the model itself. The model considers the interdependencies in the data. Source Time Series Forecasting Analysis
In Deep Learning Neural Network, each hidden layer is responsible for training the unique set of features based on the previous layer's output. As the number of hidden layers increases, the complexity and abstraction of data also increase. It forms a hierarchy of lowlevel features to highlevel features. With this, it becomes possible that the Deep Learning Algorithm helps to solve higher complex problems consisting of a vast number of nonlinear transformational layers.
Hidden Layers in Deep Learning
Deep Learning works by the architecture of the network and the optimum procedure employed by the architecture. The type of network followed is known as a Directed graph. The graph's design was so that each hidden layer is connected to every hidden node. Combination and recombination of outputs from all units of the hidden layer are in the context of the mix of their activation functions. This procedure is known as NonLinear Transformation after that optimum process is applied to the network to produce optimum weights for each layer's unit. It is the whole routine for the flow of information inside the hidden layers to produce the required target output. Too many hidden layers present in the algorithm are not feasible. It is because of the neural network's training with the simple gradient descent procedure. If a huge number of hidden layers are into the algorithm, this gradient descent will decrease, affecting the output.What is Machine Learning?
Machine Learning is a set of techniques beneficial for processing large data by developing algorithms and rules to deliver the necessary results to the user. It is the method used for developing automated machines by executing algorithms and a set of defined rules. In Machine Learning, data is fed, and the algorithm executes the set of rules. 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 automatically goes deep inside the system to automatically detect production problems.Click to explore Anomaly Detection and Monitoring Using Deep Learning
What is Deep about Deep Learning?
The traditional neural network consists of at most two layers, and this type of structure of the Neural Network is not suitable for the computation of larger networks. Therefore, a neural network that has more than 10 or even 100 layers is 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 collecting 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. We can conclude that as the data moves from the lowest layer to the highest layer (running deep inside the neural network), more abstracted information is collected.Classes of Deep Learning Architecture

Deep Learning for Unsupervised Learning

Hybrid Deep Networks
Difference Between Neural Networks and Deep Learning Neural Networks
Neural networks can use any network such as feedforward or recurrent network with 1 or 2 hidden layers. But, when the number of hidden layers increases, i.e., more than two, it is known as Deep Learning Neural Network. Neural Network is less complicated and requires more information about feature selection and feature engineering methods. On the other hand, Deep Learning Neural Network does not need any information about features; rather, they show optimum model tuning and model selection independently.Why is Deep Learning Important?
In today’s generation, the usage of smartphones and chips has increased drastically. Therefore, more and more images, text, videos, and audio are created day by day. But, as we know that a singlelayer neural network can compute complex functions. On the contrary, for the computation of complex features, Deep Learning is needed. It is because deep nets within the deep learning method can develop a complex hierarchy of concepts. Another point is that when unsupervised data is collected, and machine learning is executed, manually labeling the human being must perform data. This process is timeconsuming and expensive. Therefore, to overcome this problem, deep learning is introduced as it can identify the particular data.Introduction to Deep Learning Neural Network
Various methods are introduced to analyze log files, such as pattern recognition methods like KN Algorithm, Support Vector Machine, Naive Bayes Algorithm, etc. Due to the many 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 good 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 deep layer learning process of the sensor areas in the brain.Deep Learning Techniques
Different techniques of Deep Learning are described below 
Convolutional Neural Networks

Restricted Boltzmann Machine

Recursive Neural Network
Technologies can employ nextgeneration server infrastructure that spans immense Windows and Linux cluster environments. Source Anomaly Detection with Deep Learning
5 Amazing Applications of Deep Learning

Biological Analogs

Image Classification

Natural Language Processing

Automatic Text Generation

Drug Discovery and Data Leakage
Data Used for Deep Learning
Deep Learning can be applied to any data such as sound, video, text, time series, and images. The features requirement within the data are: The data should be relevant according to the problem statement.
 To perform the proper classification, the dataset should be labeled. In other words, labels have to be applied to the raw data set manually.
 Deep Learning accepts vectors as input. Therefore, the input data set should be in the form of vectors and the same length. This process is known as Data Processing.
 Data should be stored in one storage place, such as a file system, HDFS (Hadoop Distributed File System). If the data is stored in different locations that are not interrelated, then Data Pipeline is needed. The development and processing of the Data Pipeline is a timeconsuming task.
Deep Learning Application Areas
Deep learning neural network plays a major role in knowledge discovery, knowledge application, and last but least knowledgebased prediction. The benefits of deep learning are below  Power image recognition and tagging
 Fraud Detection
 Customer recommendations
 Used for analyzing satellite images
 Financial marketing
 Stock market prediction and much more
Read more about DevOps for Deep Learning on Kubernetes .
Deep Learning Approach for Automatic Log Analytics
How deep learning helps the analysis of log data with examples. Imagine yourself in this scenario: We have to analyze the server log to extract the information about the internal employees' events and interpret how much data is leaked from the organization's server.Solutions for Automatic Log Analytics
Deep learning
There are many existing solutions for data security, but the results produced by them are not up to mark. Therefore, the administrator of the security department examines the flow of data by analyzing the server logs. But, there is a drawback that the response time taken by the administrator is huge and efforts made for the detection of leakage of data in vain. Deep Learning provides the best results in analyzing the server log files. A system is proposed that uses Deep Learning Algorithm to examine the activities of the internal employees. First of all, the security log information is collected. This information consists of the user's information documents, personal information, along user access rights.Data Leakage
It also consists of information regarding the leakage of data from the database. The Data Leakage procedure is defined by considering both the security log list and the purpose of the data leakage. After completion, the graph develops the data leakage method. This graph describes leakage time and distinguishes the personal information of each internal employee using different color palettes. After the graphical representation of data leakage, deep learning is trained to classify the graphs into the normal and the abnormal behavior of internal employees.Deep Learning Algorithm
It is implemented using these graphs as input and comparing the similarity with the graph showing the internal employee's data leakage. After receiving the information, the administrator will examine the path of the data leakage. Now let's discuss another example about the analysis of log messages using a deep learning algorithm. Log messages consist of messages in the form of text. Traditional algorithms like support vector machine etc., do not produce optimum results while performing text classification. This is because these methods are not able to determine the semantic relationship between the words. Therefore, a deep learning algorithm known as a Recurrent Neural Network is beneficial for Log Analysis.Machine Learning is based on algorithms that can learn from data without relying on rulesbased programming. Source Executive’s guide to machine learning
The concept behind the recurrent neural network consists of a hidden layer that acts as a memory that stores the internal state of the log data. When the new data reach, the memory is updated, and decisions are made according to the current and previous input. The input layer will consist of log messages and training with the algorithm. Whenever there is abnormal behavior by the hidden layer, an alert will rise. This is one of the best approaches for the analysis of log files. Now let’s discuss how log analytics is performed in the Big Data Platform using Deep Learning. Firstly, all log data types are taken as input, such as proxy infrastructure log, DNS infrastructure log, and much more. Data Integration is performed by collecting all log data at one location.