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.
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.
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.
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!
An excerpt from Managing Machine Learning Projects by Simon Thompson
Managing Machine Learning Projects will teach you to guide machine learning projects from design to production—no machine learning experience required!
Read this article if you’re a project manager who works with machine learning applications.
An excerpt from JAX in Action by Grigory Sapunov
This excerpt covers:
What is JAX, and how does it compare to NumPy
Why use JAX?
Comparing JAX with TensorFlow/PyTorch
Read it if you’re a Python developer or machine learning practitioner who is interested in JAX and what it can do.
An excerpt from Data for All by John K Thompson
Want to stop being a passive source of free data that other people make money off of? This book is a must-read for anyone who wants to take control of their personal data. It lays out how businesses collect, use, and exploit your data (and the related dangers), and clearly explains the legislation that will overturn the existing system, and how you can use it.
It’s time to completely change your relationship with your data.
An excerpt from Data Mesh in Action by Jacek Majchrzak, Sven Balnojan, and Marian Siwiak
This excerpt explores the four principles of the Data Mesh as a concept.
Read this article if you are interested in learning what a Data Mesh is and how it is used.