I think what people want most to see is actual implementation of those stuff, not just going through the paper and explaining math.

Thanks for the suggestion, I might try to put in some simple implementations of the ideas I am presenting (in Python) depending on the concept.

Depends on the person, there's probably many people who prefer the general theoretical aspects too

I second this. Moreover, a lot of people do implementation, whereas very few (if any) go over the theory.

Disagree -- the logic behind Bayesian estimators is extremely finicky. It took me fucking *years* to wrap my head around Variational Inference and I still don't have a great intuition why MCMC works. If the theory checks out, the implementation is pretty straightforward.

Initially my idea was to just make purely theoretical videos. But I think that in some topics (where there is a clear algorithm for example) it might be helpful to show some Python implementation. Even in that case, I prefer the video overall to be more math focused. So going forward, I will add code if it seems necessary to complete the explanation. For example, in causality, the concept of avg. treatment effects can be explained through theory, but a concrete Python example will make it much clear for anyone watching what exactly is happening.

I think you need to decide who the target audience is.

If it is just for your own understanding of the topic, and you have no intent of building up a base of viewers, what you have is fine enough. It is equivalent to a math lecture found in most colleges.

But, you are here asking for feedback. Coupled with your plan for regular weekly posts to suit youtube's algorithm, this suggests you do want an audience. In which case, I would say your current target audience would likely be an expert who is looking for a reminder, or to look at how someone else presents the lecture.

If you are looking for more of an audience, you need an introduction. You are jumping straight into the details without explaining what it is you are presenting, why is this important, how or where it is used. You are not setting up a context for the viewer to get a frame of reference for what you are explaining, so it took me close to 3 minutes of watching before I even knew what it is you were even attempting to explain.

Thank you for the comment.

My target audience is people who would like to understand concepts in machine learning but want the concepts to be individually packaged as one concept per video, compared to other ML youtubers or lectures which may cover many topics in one video and can feel overwhelming. Additionally, my focus is to be as clear as possible, provide an example in certain cases, but be more math oriented as I previously mentioned. I do wish to build an audience in the future, but the quality of my videos and the Youtube channel being important to my learning process is primary. One reason for wanting to build an audience is to ensure that the videos I make are actually useful for others as well. That's why I asked for feedback.

I further agree that I need a better introduction, maybe an example of where and how this concept can be used, and why we need it. I will keep this in mind for future videos. Thanks for your feedback!

I saw a debate, I think on Stack Exchange, about why people use pseudocode.

In a situation like this, good pseudocode is much better than Python, probably. It lasts forever, is applicable to all languages, and anyone can read it if it's well written.

Speaking only in general here: often in ML, we don't know exactly why things work theoretically. Even for something like convolutional neural networks, I'm not sure if we have a complete understanding of "why" they work, or what happens internally. There have certainly been papers which brought into question our assumptions about how these things work. Adversarial images are a good example of things that we wouldn't have expected. So, in ML, sometimes the method/algorithm, and whether it works, are more important than an exact theoretical understanding of what's happening internally. You can't argue with superhuman alphago performance.