Machine Learning overview

Machine Learning vs non-Machine Learning algorithm

Recently, I was interacting with some non-technical folks and noticed that people use the term ML even when an algorithm doesn’t actually use any Machine Learning. So here is the simplest definition (that I know of) which explains the difference between Machine Learning and non-Machine Learning algorithms:

Dialogflow Machine Learning Algorithm

I get some variant of this question quite often from readers or coaching clients: “What type of machine learning is happening within the black box? Any ideas?” While the short answer is “No, I don’t since Dialogflow isn’t open source as of date”, this doesn’t mean you cannot try and reverse engineer and get as much understanding as you can by using a special tool which already exists inside Dialogflow:

Can I use GPT2 for my Dialogflow bot?

Recently, one of my clients asked me if it is possible to use GPT2 for his Dialogflow bot. Short answer: probably not.

A MUST read article on Dialogflow training phrase quality

Recently, Google published a very interesting article on how you can assess the quality of the training phrases inside your Dialogflow agent. In my view, if you are at all interested in building a Dialogflow agent, it is a MUST READ. Note: You do need a fairly good programming background, plus some basic understanding of concepts such as “word vectors” to understand that article. So maybe the person who will read it is the programmer on your team.

What is a good value for the ML Classification Threshold in Dialogflow?

This was the question I received from multiple clients recently, and I have been thinking about this question for a while. Recently, I got some time to do some research on this problem and here are my findings. If you don’t have time to read the post and just need a quick and dirty answer: just use the Default value you get when you create an agent. Here is why