Subject

Python

Function Pipelines for Mapping Complex Transformations

From Mastering Large Datasets with Python by J.T. Wolohan

This article covers

· Using map to do complex data transformations

· Chaining together small functions into pipelines

· Applying these pipelines in parallel on large datasets

Neural Network Architectures

From Probabilistic Deep Learning with Python by Oliver Dürr, Beate Sick, and Elvis Murina

This article dives into neural network architectures and how get started implementing and using them.

Working with Large Datasets Faster: using the map function

From Mastering Large Datasets by JT Wolohan

This article explores using the map function creatively in a data project.

Modern Data Solutions with Python

From Mastering Large Datasets with Python by John T. Wolohan

 

 

 

Building Linear Models with Dask ML

From Data Science at Scale with Python and Dask by Jesse C. Daniel

This article delves into building linear models using Dask-ML.

Learn Coding Best Practices in Python

From Practices of the Python Pro by Dane Hillard

 

The Towers of Hanoi

Six Questions for David Kopec, author of Classic Computer Science Problems in Python

David Kopec is Assistant Professor in computer science at Vermont’s Champlain College and author of two books in the Classic Problems series. If you want more, find @davekopec on Twitter.

Constraint-Satisfaction Problems in Python

From Classic Computer Science Problems in Python by David Kopec

A large number of problems which computational tools solve can be broadly categorized as constraint-satisfaction problems (CSPs). CSPs are composed of variables with possible values which fall into ranges known as domains. Constraints between the variables must be satisfied in order for constraint-satisfaction problems to be solved. Those three core concepts—variables, domains, and constraints—are simple to understand, and their generality underlies the wide applicabilit

Free eBook: Exploring Python Basics

Free-eBook-Ceder_EPB

Chapters selected by Naomi Ceder

Thinking in Python

Six questions with Naomi Ceder, author of The Quick Python Book, Third Edition.

Naomi Ceder earned a Ph.D in Classics, but, since 2001, has been learning, teaching, and using Python. An elected fellow of the Python Software Foundation, Naomi currently serves as chair of its board of directors. She also speaks internationally about the Python community, and on inclusion and diversity in technology in general. By day she leads a team of Python programmers for Dick Blick Art Materials, and in her spare time she enjoys sketching, knitting, and deep philosophical conversations with her dog.

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