The goal of this blog post is to describe key concepts of how Hyperledger Fabric blockchain works so that you can get a basic understanding of how to use.
Hyperledger is the umbrella open source project that the Linux Foundation has created and hosted since 2015.
Before getting into building the blockchain, you need to understand some of the basics of blockchain.
As the name suggests, A Blockchain is a “chain of Blocks”. Each block contains a “Block Number”, “Nonce”, “Information (data)” where particular transaction takes place, “Previous Hash” and, “hash”.
All the Blocks in the Blockchain are linked to each other with the “hash” variable. A “hash” contains information of the previous block in the chain and that’s what keeps the entire chain-linked and connected. …
From Blockchain introduction to working, types, characteristics and learning new terms related to Blockchain technology
Blockchain always seems to be complicated, but it’s quite a simple technique. Blockchain is a database, which was invented by a person (or group of people) using the name Satoshi Nakamoto in 2008 to serve as the public transaction ledger of the cryptocurrency bitcoin. As, identity of Satoshi Nakamoto remains unknown to date. Let’s understand Blockchain in detail.
Blockchain as named referred “Chain of Blocks” also known as Satoshi’s Bitcoin Model. It’s a first digital currency to solve the double spending problem without a need…
In this article, I am going to tell about how to carry out customer segmentation and other related analysis on online retail data using python.
Let’s Understand what is Customer Segmentation
It is a practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests and spending habits.
Key differentiators that divide customers into groups:
· Demographics (age, race, religion, gender, family size, ethnicity, income, education level)
· Geography (location)
· Psychographic (social class, lifestyle and personality characteristics)
· Behavioural (spending, consumption, usage and desired benefits) tendencies…
The name itself says “Pixel to Pixel” which means, in an image it takes a pixel, then convert that into another pixel.
The goal of this model is to convert from one image to another image, in other words the goal is to learn the mapping from an input image to an output image.
But why and what application we can think of ??
Well, there are tons of applications we can think of:
The Pix2Pix GAN has been demonstrated on a range of image-to-image translation tasks such as converting maps to satellite photographs, black and white photographs to color…
In this article, you will learn about GloVe, a very powerful word vector learning technique. In this work we present a step-by-step implementation of training a Language Model (LM), using Long Short-Term Memory (LSTM) and pre-trained GloVe word embeddings.
Language Modeling is an important task in many Natural Language Processing (NLP) applications. These applications include clustering, information retrieval, machine translation, spelling and grammatical errors correction. In general, a language model defined as a function that puts a probability measure over strings drawn from some vocabulary.
The statistics of word occurrences in a corpus is the primary source of information available…
Data preparation is required when working with neural network and deep learning models. Increasingly data augmentation is also required on more complex object recognition tasks.
Data augmentation means to increase the amount of data. In, other words, having a larger dataset means a more robust model. But acquiring more data cannot always be as easy and there may be a problem of storing them and feeding it to model.
To mitigate this problem, we can manually increase the data by doing some changes or we can make use of one of Keras image preprocessing class that can do this in…
Building machine learning models that produce high accuracy is getting easier, but when it comes to interpretability, most of them are still far from good. In many cases, you might need to put more emphasis on understanding the models than accuracy.
As a powerful yet simple technique, generalized additive model (GAM) is underrepresented. Only few people know apply it in their daily work. To understand how GAM works in R. I have performed method like lm() function for linear regression and gam() function. Firstly, lets understand what is exactly GAMs.
Whenever we build statistical models, we face a trade-off between…
Those who are not familiar with “CAPTACHA” these are weird little quizzes you end up taking while landing on a website where you have to type-in jumbled sequence of words, numbers and letters to prove that you are not a bot.
We’ll discuss here history behind they came to be and the problem they are gonna solve, what implication it has and ethics involved.
An acronym for “completely automated public Turing test to tell computers and humans apart” coined in 2000 which tests whether you are human or not. So, Turing Test was named after Alan Turing who was computer…
The ultimate search engine would basically understand everything in the world, and it would always give you the right thing. Basically, it works like a semantic search which refers to search process: understanding the query, instead of simply finding literal matches,or representing knowledge in a way suitable for meaningful retrieval.
Examine the search keywords and kinds of words, phrases are being used in the portal. Analyze your analytics. Techniques which could be implemented and will be good fit will the Predefined GloVe Embeddings on your dataset. The alorithm will give the vector representations of the words. Train the GloVe embedding…
Data Scientist, Pursuing PGDM-Big Data Anaytics from Goa Institute of Management, Computer Science Engineer.