An excerpt from the Tao of Microservices, Second Edition by Richard Rodger
If you want to really climb inside the microservice architecture and learn how it works, you have to use it in practice. This updated edition will teach you the theory underlying microservice-based systems by having you use them in practice.
Read on for more.
An excerpt from Julia for Data Analysis by Bogumil Kaminski
This article dives into working with data in dataframes with Julia.
Read it if you’re a data scientist or anyone who works with lots of data, and if you’re interested in the Julia language.
An excerpt from Julia as a Second Language by Erik Engheim
This article covers:
· Storing values on keys in dictionaries.
· Working with pair objects.
· Using tuples to create dictionaries.
· Comparing dictionaries and arrays.
Read it if you’re interested in the Julia language or in how it handles dictionaries.
An excerpt from Julia for Data Analysis by Bogumil Kaminski
Many languages designed for doing data science provide ways to perform vectorized operations, which is also often called broadcasting. In Julia, broadcasting is also supported. In this article you will see how to use it.
Read it if you’re a data scientist or anyone who works with lots of data, and if you’re interested in the Julia language.
With Tiny Node Projects by Jonathan Wexler
Learn Node.js or take your existing skills up a notch with this book.
From Causal Inference in Data Science by Aleix Ruiz de Villa
Causal inference models predict why something will happen, i.e. causal effects, rather than the outcomes themselves. This is useful in many instances and is a budding field in machine learning and data science.
Read on to see how it works and what you will learn from this book.
With Learning C++ by Ruth Haephrati and Michael Haephrati
C++ has been one of the top programming languages for 30 years and it’s not slowing down. It’s never a bad time to learn it, and this book will help you do just that—even if you don’t have any programming or computer science experience.
Read on to find out more.
From Causal Machine Learning by Robert Ness
Enhance machine learning with causal reasoning to get more robust and explainable outcomes. Power causal inference with machine learning to create next gen AI..There has never been a better time to get into building causal AI.
Read on for more.
From Think like a CTO by Alan Williamson
If you aspire to be a CTO or are working as one, this book is for you. The CTO position is not well defined and this book will help you prepare for or carry out work as a CTO. It will also help you (the CEO, CFO, or COO) decide what sort of professional you want to hire as a CTO and what their job should be.
Read on to find out more.
An excerpt from Bayesian Optimization in Action by Quan Nguyen
What is Bayesian Optimization? What problem(s) does it propose to solve? If you deal with Machine Learning in your job and you’re running into problems with things like black box optimization and hyperparameter tuning, then Bayesian optimization is something you should learn more about.
Read on if you want to learn more. Bayesian optimization isn’t as difficult as you might think!