It uses the Akka framework as the reference implementation and demonstrates how advanced architectural patterns like event sourcing and CQRS can be put to great use in implementing scalable models. On the subject of reactive modeling, the book focuses on higher order concurrency patterns like actors and futures. The book demonstrates how advanced FP patterns like algebraic data types, typeclass based design, and isolation of side-effects can make your model compose for readability and verifiability. You will start with the basics of functional programming and gradually progress to the advanced concepts and patterns that you need to know to implement complex domain models.
This books is recommended for machine learning practitioners, data scientists, statisticians and also for stakeholders deciding on the use of machine learning and intelligent algorithms.įrdomain - Code repo for Functional and Reactive Domain ModelingĬode repo for Functional and Reactive Domain Modeling.Functional and Reactive Domain Modeling teaches you how to think of the domain model in terms of pure functions and how to compose them to build larger abstractions. In an ideal future, machines will be able to explain their decisions and make a transition into an algorithmic age more human. The later chapters focus on analyzing complex models and their decisions. In the first chapter algorithms that produce simple, interpretable models are introduced together with instructions how to interpret the output. Did it learn generalizable features? Or are there some odd artifacts in the training data which the algorithm picked up? This book will give an overview over techniques that can be used to make black boxes as transparent as possible and explain decisions. As the programmer of an algorithm you want to know whether you can trust the learned model.
An explanation increases the trust in the decision and in the machine learning model. Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. This book is about interpretable machine learning. Interpretable-ml-book - Book about interpretable machine learningĮxplaining the decisions and behaviour of machine learning models. So, I wanted to create an online reference where people could come to learn specifically about this issue and why they might not actually need class syntax in JavaScript. I share this sentiment, but I have encountered quite a few programmers in the wild who don't agree or simply don't seem to understand why some of us have this opinion. While ES6 brings several useful and syntactically pleasing new features to JavaScript, there are many people in the JS community who feel that adding class syntax to the language was a mistake. Reverse-inspired by all of the awesome lists on GitHub, like Awesome, Awesome Awesomeness, Awesome JavaScript, Awesome React, Awesome Cycle.js, Awesome Go, Awesome Elixir, Awesome Elm, etc. Not-awesome-es6-classes - A curated list of resources on why ES6 (aka ES2015) classes are NOT awesome