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Basics of Blockchain Technology

Blockchain technology has gained popularity in recent times. This article covers some of the fundamental concepts associated with it, including: What is blockchain? Why do we need blockchain? How does blockchain ensure trust? Who invented it? When to use it? When not to use it? So, let’s get started. What

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The Threat Environment

People who don’t have to do it seem to not understand the nature of the cyber threat. This very short summary is intended for everyone who is responsible for it to hand to everyone else to help start a conversation and gain mutual understanding. The 6 questions people are commonly

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Experiential Learning

AI, Neural Nets, and Complexity of the Brain Quantum cryptography and its real effect on current systems Will training help to counter influence operations? Blockchain, distributed ledger, and crypto-currency Don’t trust zero trust Basics of complexity and granularity and tradeoffs of space and time Granularity of control and adding dimensions

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Some results in cybersecurity and why they may be interesting

Every once in a while I come across something interesting with substantial potential impacts but that differs from the common misconceptions. Many of them I point out with a fevered disdain of foolishness, while others I view more philosophically. This article is about some recent results that I think are

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Deep Learning Basics

Deep learning is a powerful machine learning technique. These are widely used in Natural Language Processing (NLP) image/speech recognition robotics and many other artificial intelligence

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Confusion Matrix

Confusion Matrix, Accuracy, Precision, Recall, F score explained with an example In this post, we will learn about What is accuracy What are precision, recall,

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Pre-processing

Data Preprocessing – Creating Dummy Variables and Converting Ordinal Variables to Numbers with Examples Data cleaning is a critical step before fitting any statistical model.

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Missing Values

Handling Missing Values in Python In this post, we will discuss: How to check for missing values Different methods to handle missing values Real life

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Time Series Components

Components of Time Series In this post, let us explore the four components of time series data. Trend (T) Cyclicality (C) Seasonality (S) Irregular component

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ARIMA/SARIMA with Python

Autoregressive Integrated Moving Average (ARIMA) is a popular time series forecasting model. It is used in forecasting time series variable such as price, sales, production,

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