Book Image

Mastering Ethereum

By : Merunas Grincalaitis
Book Image

Mastering Ethereum

By: Merunas Grincalaitis

Overview of this book

Ethereum is one of the commonly used platforms for building blockchain applications. It's a decentralized platform for applications that can run exactly as programmed without being affected by fraud, censorship, or third-party interference. This book will give you a deep understanding of how blockchain works so that you can discover the entire ecosystem, core components, and its implementations. You will get started by understanding how to configure and work with various Ethereum protocols for developing dApps. Next, you will learn to code and create powerful smart contracts that scale with Solidity and Vyper. You will then explore the building blocks of the dApps architecture, and gain insights on how to create your own dApp through a variety of real-world examples. The book will even guide you on how to deploy your dApps on multiple Ethereum instances with the required best practices and techniques. The next few chapters will delve into advanced topics such as, building advanced smart contracts and multi-page frontends using Ethereum blockchain. You will also focus on implementing machine learning techniques to build decentralized autonomous applications, in addition to covering several use cases across a variety of domains such as, social media and e-commerce. By the end of this book, you will have the expertise you need to build decentralized autonomous applications confidently.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: Blockchain - Ethereum Refresher
5
Section 2: Decentralized Application Development Workflow
12
Section 3: Ethereum Implementations

Understanding machine learning

Machine learning (ML) is a subset of artificial intelligence (AI), which in turn is a field in the broader subject of data science. ML is focused on creating programs that learn by themselves to solve specific problems without having to write all the logic; we just need to give them lots of input. Trial and error is the main mechanism with the machine slowly learns how to achieve the right output to a problem.

The moment computers were created was the moment scientists asked themselves, "How can we make this machine think and act as a human?". That's why understanding how computers learn begins with understanding how humans see the world.

Think about it for a second: how do you think animals and humans learn to survive in the dangerous and confusing world we live in? By learning from others? Well, that's a valid learning system...