Part 4: Recurrent Neural Networks(RNN)

This article, mainly discussing the Recurrent Neural Network(RNN) which is one of the main part of Supervised Learning with neural networks working with time series data.

(1) What are Recurrent Neural Networks?

  • A recurrent neural network (RNN) is a type of artificial neural network which uses sequential data or time series data.
  • These deep learning algorithms are commonly used for ordinal or temporal problems with RNN.
  • Derived from feedforward networks, RNNs exhibit similar behavior to how human brains function.


Part 3: Convolutional Neural Networks (CNN)

This article, mainly discussing the Convolutional Neural Network(CNN) which is one of the main part of Supervised Learning with neural networks.

(1) What are Convolutional Neural Networks?

  • Convolutional Neural Networks are designed to address image recognition systems and classification problems.
  • Convolutional Neural Networks, like FFNNs, is made up of neurons with learnable weights and biases. Each neuron receives several inputs, takes a weighted sum over them, passes it through an activation function, and responds with an output.
  • The whole network has a loss function and all the tips and tricks that we developed for…


Part 2: Feed-Forward Neural Network (FFNN)

This article, mainly discussing Feed Forward Neural Network(FFNN) which is commonly used in Supervised Learning with neural networks.

(1) In Supervised Learning Model

  • Normally in the Supervised Learning model, there are mainly 4 types Feed Forward Neural Network(FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network(RNN), and Encoder-Decoder Architectures.
  • The main purpose of supervised learning neural networks is, give predictions by training with past data set for specific tasks. (Trainable)


Part 1: Deep Learning Introduction

In this article, we are talking about the Introduction of Deep Learning which related to neural networks.

(1) What is Deep learning?

  • Deep learning is a subset of Machine learning in which multi-layered neural networks learn from a vast amount of data.
  • It is a high-level version of Machine learning which uses Artificial Neural Networks as trainable algorithms.

(2) What are the applications of the Deep learning concept?

  • Face Recognition
  • Self Driving Cars.
  • Image Classification
  • Medical Diagnosis
  • Ads, Search social Recommendations


Part 2- Data Wrangling & Preprocessing

  1. Where does data come from?
  • Data can come from various ways, likewise from proprietary data sources, Government data sets, Web search, Academic data sets, Sensor data & Crowdsourcing, By researcher (Creating own datasets), etc.

2. If there is bad data, what happens then?

  • If there is bad data, we can get bad results, which can affect the whole process, for final output also.
  • Incorrect analysis can happen, invalid insights, poor outcomes, wrong decisions can be taken, likewise, several issues can happen.

3. What Is Data Wrangling?

  • Data Wrangling (Data Munging) is the process of…


Part 1 — Python Basic related to Data Science

1.What is Python?

Python is a very popular interpreted, high-level, general-purpose programming language. Python’s design philosophy emphasizes code readability with its notable use of significant whitespace

Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

2. Python IDEs

There are popular IDEs that can use with python related to the Data science field. An IDE (Integrated Development Environment) understands your code much better than a text editor. It usually provides features such as build automation, code linting, testing, and debugging.

E.g…


The goal of this is to show you how to get a Node.js application into a Docker container. In the first part of this guide, we will create a simple web application in Node.js, then we will build a Docker image for that application, and lastly, we will instantiate a container from that image.

An image is a blueprint for a container, a container is a running instance of an image normally. Let’s try to dockerize a node.js application step by step.

1. Create the Node.js app

First, open visual studio code and create and open a new folder to the making the application. Then…


Web development has become one of the most innovative and important areas of the IT industry. This article aims to demonstrate the principles, concepts, and technologies surrounding full-stack web development.

What is a “Full-Stack” Web Developer?

A full-stack web developer is a person who can develop both client and server software, also knows how to

  • Program a browser (like using JavaScript, jQuery, Angular, or Vue)
  • Program a server (like using PHP, ASP, Python, or Node)
  • Program a database (like using SQL, SQLite, or MongoDB)

According to that, the client-side is called Front-end, and the server-side is called Back-end.


This article is about security scanning vulnerabilities detection tools for secure DevOps. In Part 1, were discussed image scanning tools. In Part 2 we are discussing about Run time security scanning tools for secure DevOps.

There are many runtime scanning tools which help to detect application vulnerabilities in DevOps. Some of them are Sysdig, Sysdig Inspect, Falco, Kube-bench, Kube-hunter & Prometheus.

1. Sysdig


This article is about security scanning vulnerabilities detection tools for secure DevOps. It can be used DevOps security tools in environment to identify vulnerabilities & security flaws in the early stages of development applications.

Security testing has always been an important step in the application development process. Yet, traditional measures often occur too late in the process to effectively find and fix vulnerabilities before causing costly production delays.

Securing code in a DevOps development environment requires managing the software supply chain and checking the security of common components and frameworks, adopting a framework to automate testing and need to analyze…

Udara Vimukthi

Final Year Undergraduate, University of Moratuwa.

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