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Showing posts from October, 2018

Learn Blockchains by Building One

You’re here because, like me, you’re psyched about the rise of Cryptocurrencies. And you want to know how Blockchains work—the fundamental technology behind them. But understanding Blockchains isn’t easy—or at least wasn’t for me. I trudged through dense videos, followed porous tutorials, and dealt with the amplified frustration of too few examples. I like learning by doing. It forces me to deal with the subject matter at a code level, which gets it sticking. If you do the same, at the end of this guide you’ll have a functioning Blockchain with a solid grasp of how they work. Before you get started… Remember that a blockchain is an immutable, sequential chain of records called Blocks. They can contain transactions, files or any data you like, really. But the important thing is that they’re chained together using hashes . If you aren’t sure what a hash is, here’s an explanation . Who is this guide aimed at? You should be comfy reading and writing some basic Python, as well a

Machine Learning: Black Magic to Science : A gentle introduction to Machine Learning

What is it all about? Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. Essentially, it is a method of teaching computers to make and improve predictions based on some data. Artificial intelligence is a state of machine when it can take decisions just like a human. The hypothesis is basic, machines take information and ‘learn’ for themselves. Machine learning is a subset of AI. In the previous decade, machine learning has given us self-driving automobiles, common-sense discourse acknowledgment, and an enhanced comprehension of the human genome. Machine learning is so unavoidable today that you most likely utilize it many times each day without knowing it. This blog has been divided into three parts written by me, Shivansh Beohar and Shashwat Srivastava respectively: The Road Mo