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The book is intended to serve both as a reference manual and as a cover-to-cover exploration of Ethereum. If you're looking to get started with the Ethereum protocol open source developers, integrators, etc. It offers an introduction to concepts of probability theory, probability distributions relevant in the applied sciences, and focuses on real engineering applications, as well as basics of sampling distributions, estimation and hypothesis testing. It explores several techniques, tools, and best practices for developing and maintaining web apps that provide the privacy and security that every user needs - and deserves. Learn how web tracking works, and how to provide users with greater privacy controls. This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications.
This book starts off by showing you the main features of Cosmos DB, their supported NoSQL data models and the foundations of its scalable and distributed architecture.
This book provides a guide to this latest VS product, paying particular attention to the Integrated Development Environment, the workspace, and all the integrated tools you can leverage to build high-quality, professional applications for mobile devices and web.
This text covers the important elementary topics of graph theory and its applications. In addition, he presents a large variety of proofs designed to strengthen mathematical techniques and offers challenging opportunities to have fun with mathematics. This book is for programmers, scientists, or engineers, who have basic Python knowledge and would like to be able to do numerical computations with Python.
It will give you a solid foundation in NumPy arrays and universal functions. Beginning with detailed instructions on how to install and configure the Android SDK, Studio, and Virtual Device Manager, More advanced topics such as database management, content providers and intents are also covered, both video and audio. This book aims to enable the reader to quickly acquire a Python foundation.
In this book, author walks Java developers through the creation of a complete reactive microservices-based system. The result? A system that's easier to deploy, manage, and scale than a typical Java EE-based infrastructure.
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This is the lecture notes for a course on Python and coding theory designed for students who have little or no programmig experience. You will learn some of the Python computer programming language and selected topics in coding theory. Such rules affect app architecture and library design. This book is a software engineer's notes on problem solving. Ideally suited as practice material for coding interviews.
This started out as a collection of interview questions and solutions that suthor accumulated over the years. This book uses Python to introduce folks to programming and algorithmic thinking. It is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.
Book Site. How many runways in a particular airport? Click here to find out. Mastering Ethereum: Training a neural networks with single hidden layer with feed-forward type for regression or classification. Show All 1, Learn the basics of how to create, edit, and simulate Simulink models through an interactive tutorial.
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Show All Show All 2, Show All 16, Plot lots of lines with very distinguishable and aesthetically pleasing colors. Works for 'N' colors. Show All 13, Choose a web site to get translated content where available and see local events and offers.
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In this code a full version of denoising autoencoder is presented. And, of course, people want free ebooks. With that in mind, here's a list of 10 free machine learning and data science titles to get your spring reading started right. What better way to enjoy this spring weather than with some free machine learning and data science ebooks? Here is a quick collection of such books to start your fair weather study off on the right foot. The list begins with a base of statistics, moves on to machine learning foundations, progresses to a few bigger picture titles, has a quick look at an advanced topic or 2, and ends off with something that brings it all together.
A mix of classic and contemporary titles, hopefully you find something new to you and of interest here. Think Stats: Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.
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The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference.
In fact, this was the author's own prior opinion. Understanding Machine Learning: Machine learning is one of the fastest growing areas of computer science, with far-reaching applications.
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