May 20 • 7M

A machine learning cookbook?

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Don’t panic! We are going to take this topic in two different directions this week. First, the obvious path will be taken and we will talk about programming, code, and python cookbooks. Second, for those of you that were hoping this week will be a deep dive into artificial intelligence and machine learning derived recipes compiled into a cookbook you will not be disappointed either. Another way to talk about what we are covering today would be that not only are we talking python cookbooks, but also we are covering the epicurean adventures that could be created. 

Beginning with the first topic under consideration; coding related answers to the machine learning cookbook question are real. What exactly is a Python Cookbook? If you are a fan of the work by David Beazley and the 3rd edition of the Python Cookbook from the publisher O’Reilly, then you might already have an idea [1]. I personally did not have a copy of this book on my shelf or any of the other python cookbook compilations. These are compilations of code brought together like recipes to help people learn how to use and deploy Python as a computer programming language. You can get to all the code examples that David Beazley has shared on GitHub [2]. For those of you looking to dig into some Python code and learn about how it works, a collection of recipes compiled into a cookbook is a decent place to start. You might get lucky enough to find some recipes on the specific thing you are trying to solve. The other book that stood out to me was the Modern Python Cookbook from Steven F. Lott [3]. The code from that book is also published on GitHub for easy access [4].

At this point we are pivoting to the second question about actual recipes. You might remember back to 2017 when Eric Schmidt shared an AI created cookie recipe built out by engineers at Google as part of a real world challenge [5]. You can find the recipe for the “chocolate chip and cardamom cookie” via the link above. Getting to that point apparently took 59 recipe iterations which is interesting. Oddly enough that article also brought in some of the warning elements that Eric Schmidt has shared about the potential misuse of AI. Cookie related AI examples have improved since 2017 and you could check out the Nestle Toll House cookie expert or alternatively the “cookie coach” AI now to help answer your baking questions [6]. It got announced on Twitter and they had a fun video about working with Ruth the cookie coach. 

It does appear that Ruth the Nestle Toll House cookie coach really is still online and working over at From Eric Schimdit sharing recipes to interactive chat experiences people have really opened the door to recipe driving artificial intelligence when it comes to cookies. At this point it might be a good idea to move beyond cookies and look at recipes in general. You could go out to the Google Cloud blog and learn about “Baking recipes made by AI” from December 15, 2020 [7]. The embedded video digs into making recipes with ML and is a 7 minute journey of fun featuring Sara Robinson. You may already know that during the pandemic Sara Robinson collected a ton of recipes and used a TensorFlow model to make predictions on future iterations [8]. Between the code shared on the Google Cloud blog and by Sara Robinson you can actually work on a similar cookie effort to make your own machine learning model to work on recipes. 

Our friends at Google are not the only ones that have started to wonder about using machine learning for the practical activity of creating recipes. The team over at Towards Data Science have walked through the good and the bad of the process as well within an article titled, “Using machine learning to generate recipes that actually work” [9]. This article really walks you through the process and it is interesting. People really do seem to be having fun with using ML to create recipes. Another way to take a look at the process would be with an article from KDNuggets called, “Generating cooking recipes using TensorFlow and LSTM Recurrent Neural Network: A step-by-step guide” [10]. As you can tell from the number of sources of examples on how to start making your own recipes with machine learning it is a topic that people really seem to be passionate about. Some of that passion is translating further into action. Consider the article, “Forage: Optimizing Food Use With Machine Learning Generated Recipes” [11]. Within that article Angelica, Elbert, and Brian take a real look at reducing potential food waste with machine learning. It is an interesting way to apply machine learning to a practical real world problem. 

My efforts to find a real cookbook for sale that is made up exclusively of AI/ML made recipes were not successful this week. You can find websites like that help you search recipes and build meal plans, but I did not find a cookbook you could buy from a bookstore. 

Links and thoughts:

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Christina Warren this week reminds us that the Download has moved over to 

“More WordPress Add-on Trouble”

Top 5 Tweets of the week:













What’s next for The Lindahl Letter?

  • Week 70: ML and Web3 (decentralized internet)

  • Week 71: What are the best ML newsletters?

  • Week 72: Open source machine learning security

  • Week 73: Symbolic machine learning

  • Week 74: ML content automation

I’ll try to keep the what’s next list for The Lindahl Letter forward looking with at least five weeks of posts in planning or review. If you enjoyed this content, then please take a moment and share it with a friend. Thank you and enjoy the week ahead.