- Deploy your PyTorch model to Production - Mar 20, 2019.
This tutorial aims to teach you how to deploy your recently trained model in PyTorch as an API using Python.
- 4 Reasons Why Your Machine Learning Code is Probably Bad - Feb 26, 2019.
Your current ML workflow probably chains together several functions executed linearly. Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. That is your data science workflow should be a DAG.
- How to Setup a Python Environment for Machine Learning - Feb 18, 2019.
In this tutorial, you will learn how to set up a stable Python Machine Learning development environment. You’ll be able to get right down into the ML and never have to worry about installing packages ever again.
- Python Patterns: max Instead of if - Jan 10, 2019.
I often have to loop over a set of objects to find the one with the greatest score. You can use an if statement and a placeholder, but there are more elegant ways!
- How Different are Conventional Programming and Machine Learning? - Dec 10, 2018.
When I heard about Machine Learning I couldn't contain the amazement. I was not able to get my mind around the fact, that unlike normal software programs - which I was accustomed to - I wouldn't even have to teach a computer the "how" in detail about all the future scenarios up front.
- Here are the most popular Python IDEs / Editors - Dec 7, 2018.
We report on the most popular IDE and Editors, based on our poll. Jupyter is the favorite across all regions and employment types, but there is competition for no. 2 and no. 3 spots.
- What is the Best Python IDE for Data Science? - Nov 14, 2018.
Before you start learning Python, choose the IDE that suits you the best. We examine many available tools, their pros and cons, and suggest how to choose the best Python IDE for you.
- Get a 2–6x Speed-up on Your Data Pre-processing with Python - Oct 23, 2018.
Get a 2–6x speed-up on your pre-processing with these 3 lines of code!
- 5 “Clean Code” Tips That Will Dramatically Improve Your Productivity - Oct 15, 2018.
TL;DR: If it isn’t tested, it’s broken; Choose meaningful names; Classes and functions should be small and obey the Single Responsibility Principle (SRP); Catch and handle exceptions, even if you don’t think you need to; Logs, logs, logs
- Are Vectorized Random Number Generators Actually Useful? - Aug 28, 2018.
I reported that you can multiply the speed of common (fast) random number generators such as PCG and xorshift128+ by a factor of three or four by vectorizing them using SIMD instructions. Is this actually useful in practice?
- Programming Best Practices For Data Science - Aug 7, 2018.
In this post, I'll go over the two mindsets most people switch between when doing programming work specifically for data science: the prototype mindset and the production mindset.
- Cookiecutter Data Science: How to Organize Your Data Science Project - Jul 24, 2018.
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
- Python Regular Expressions Cheat Sheet - Apr 19, 2018.
The tough thing about learning data is remembering all the syntax. While at Dataquest we advocate getting used to consulting the Python documentation, sometimes it's nice to have a handy reference, so we've put together this cheat sheet to help you out!
- R Fundamentals: Building a Simple Grade Calculator - Mar 19, 2018.
In this tutorial, we'll teach you the basics of R by building a simple grade calculator. While we do not assume any R-specific knowledge, you should be familiar with general programming concepts.
Pages: 1 2
- Introduction to Functional Programming in Python - Feb 28, 2018.
Python facilitates different approaches to writing code, and while an object-oriented approach is common, an alternative and useful style of writing code is functional programming.
Pages: 1 2
- Data Structures Related to Machine Learning Algorithms - Jan 30, 2018.
If you want to solve some real-world problems and design a cool product or algorithm, then having machine learning skills is not enough. You would need good working knowledge of data structures.
Pages: 1 2
- How To Become a 10x Data Scientist, part 2 - Sep 19, 2017.
A 10x developer is someone who is 10 times more productive than average. We adapt tips and tricks from the developer community to help you become a more proficient data scientist loved by team members, including code design and selecting right tools for the job.
- How To Become a 10x Data Scientist, part 1 - Sep 18, 2017.
A 10x developer is someone who is 10 times more productive than average. We adapt tips and tricks from the developer community to help you become a more proficient data scientist loved by team members and stakeholders.
- Data Scientists: What They Do and How to Become One - Aug 29, 2017.
Data science is growing, and will continue to grow for the foreseeable future. Whether you are a student or an expert, here are courses to help further your knowledge of this promising field.
- Top /r/MachineLearning Posts, July: Friendly Suggestions re: Coding Practices; Racist AI How-To Without Really Trying - Aug 10, 2017.
Why can't you guys comment your f*cking code?; Train Chrome's Trex character to play independently; How to make a racist AI without really trying; Is training a NN to mimic a closed-source library legal?; 37 Reasons why your NN is not working
- How Not To Program the TensorFlow Graph - May 1, 2017.
Using TensorFlow from Python is like using Python to program another computer. Being thoughtful about the graphs you construct can help you avoid confusion and costly performance problems.
- Moving from R to Python: The Libraries You Need to Know - Feb 24, 2017.
Are you considering making a move from R to Python? Here are the libraries you need to know, how they stack up to their R contemporaries, and why you should learn them.
- Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms - Jan 25, 2017.
Interested in learning machine learning algorithms by implementing them from scratch? Need a good set of examples to work from? Check out this post with links to minimal and clean implementations of various algorithms.
- Top KDnuggets tweets, Jan 04-10: Cartoon: When Self-Driving Car takes you too far; A massive collection of free programming books - Jan 11, 2017.
Also AI #DataScience #MachineLearning: Main Developments 2016, Key Trends 2017; Scikit-Learn Cheat Sheet: #Python #MachineLearning
- Data Science 101: How to get good at R - Nov 1, 2016.
Everybody talks about R programming, how to learn, how to be good at it. But in this article, Ari Lamstein tells us his story about why and how he started with R along with how to publish, market and monetise R projects.
- Is Your Code Good Enough to Call Yourself a Data Scientist? - Oct 28, 2016.
Is your code good enough to be calling yourself a Data Scientist? Figure out how to determine the answer to this question... and gain some suggestions on ensuring that the answer is "yes!"
- What Data Scientists Can Learn From Qualitative Research - Jul 14, 2016.
Learn what data scientists can learn from qualitative researchers when it comes to analysing text, and how this relates to writing quality code.
- Top KDnuggets tweets, Mar 30 – Apr 05: Top 10 Essential Books for #Data Enthusiast; If Hollywood Made #Movies About #MachineLearning - Apr 6, 2016.
Top 10 Essential Books for #Data Enthusiast; If Hollywood Made #Movies About #MachineLearning; Learning to Code Diminishing Returns - coders training their digital replacements.
- KDnuggets™ News 15:n35, Oct 28: Data Science Machine; Programming: Python vs R; KDnuggets Addiction - Oct 28, 2015.
The Data Science Machine, or 'How To Engineer Feature Engineering'; Data Science Programming: Python vs R; Cartoon: KDnuggets Addiction; How to Use Data Visualizations to Win Over Your Audience.
- EARL2015 Conference for users, developers of R, London (Sep 14-16) and Boston (Nov 2-4) - Aug 14, 2015.
The primary focus of both London and Boston Conferences is the commercial use and application of R across a broad range of business sectors.
- R Programming: Who, Where and What - Aug 11, 2015.
The “sexiest job” has the sexiest demand, and R is one of their leading weapons. Here, we are trying to capture how these unicorns are distributed, and also where you can move if you want to have great opportunities.
- Perfume, computer programming, and Harvard - Oct 8, 2014.
What is the connection between Perfume, computer programming, and Harvard education? Peter Bruce explains.
- COMAD India Graph Mining Programming contest - Sep 23, 2014.
Compete in the graph mining programming competition at COMAD 2014 and apply your skills to finding subcommunities in networks. Registration deadline is October 15th, and code must be submitted by October 27th.
- Hiring Data Scientists: What to look for? - Sep 9, 2014.
Know key characteristics of what makes up a good data scientist based upon the three authors’ consulting and research experience, having collaborated with many companies world-wide on the topics of big data and analytics.
- New Poll: What languages you used for analytics / data mining / data science work in 2014? - Aug 6, 2014.
New KDnuggets Poll is asking: What languages you used for analytics / data mining / data science work in 2014? Please vote.
- 9 Free Books for Learning Data Mining and Data Analysis - Apr 29, 2014.
Whether you are learning data science for the first time or refreshing your memory or catching up on latest trends, these free books will help you excel through self-study.
- Wolfram Breakthrough Knowledge-based Programming Language – what it means for Data Science? - Mar 2, 2014.
The coming Wolfram Programming language, 30 years in making, will probably be the largest, most comprehensive, and most knowledge-based programming language ever, and can be a significant advance for data science.