Explainable AI Cheat Sheet - Five Key Categories

[@prasadjayanti]

I am a data scientist & really appreciate your work ! Keep good work going !

[@mrunalinigarud1162]

Best blog and guidance video for AI

[@kartavyabhatt7818]

Thank you very much for the video. 9:57 yeah a dedicated video for each of the methods will be really great!

[@its_me7363]

Now I think it would be great if Jay can make video on SHAP explanability and usage...hope you have time to accept this request.

[@NishantKumar-mp9zg]

+1 I'll also be looking forward to it.

[@arp_ai]

I'd certainly love to learn more about it at some point

[@its_me7363]

@@arp_ai will wait for your video for this topic

[@kokoko5690]

Thank you for ur video it's really well organized and easy to understand

[@metallica42425]

Really appreciate these resources! Thanks for always explaining things so clearly!

[@jeanpauldelamarre6583]

Explainable AI is not only based on neural networks. Everyone wants to make neural networks explainable which is not the case by design. You also have to consider other types of models like rule based models (expert systems) or even probabilistic models which are explainable by design.

[@sheldonsebastian7232]

Found this channel via Linkedin post. It was a good find!

[@incase3007]

great video. highly informative !

[@Ninaadiaries]

Thanks for the information. It was helpful for me :)

[@TusharKale9]

Very important topic covered in good details. Thank you

[@palomoshoeshoe8985]

Thank you so much for your contribution, i really appreciate it.

[@camoha8313]

Thanks for this video (love the john coltrane )

[@francistembo650]

Thanks man!

[@mpalaourg8597]

Nice video! But even better the resources which were referenced! Thank you...

[@dev0nul162]

Thank you for what you have provided here! The links are providing tremendous added value to your videos.

[@omyeues]

Very interesting ! Thank you for sharing

[@chathurijayaweera1590]

Very informative and easily understandable. Thank you for making this video

[@TheSiddhartha2u]

Thank You for nice and easy information. I was looking for such information 👍

[@muhammadomar9552]

Thanks for knowledge sharing. Where decision trees lie in cheat sheet?

[@ottunrasheed4076]

Interesting content. I am looking forward to the paper reading videos

[@raminbakhtiyari5429]

just fascinating

[@MeriJ-ze5dd]

Thanks Jay. Amazing video. I have a question though: why the pretraining in gpt-3 is called unsupervised learning?it works on labelled data, so I think it should be a supervised learning task.

[@arp_ai]

It's better called self-supervised learning nowadays. It's unsupervised in the same way that word2vec is unsupervised -- it is not trained on an explicitly labeled dataset, but rather on on examples generated from free text.

[@sanjanasuresh5565]

Hello! Can you please make a video on interpretability of unsupervised ML models

[@balapranav5364]

Hi sir, Shap gives you the info same like feature importance results?

[@arp_ai]

SHAP is a method of obtaining feature importance, yes.

[@deepbayes6808]

Why logistics or linear regression are considered interpretable? If you have 1000x of features, how can you interpret a non-sparse weight matrix?

[@juanpablopajaro9229]

Jay, I was exploring SHAP for explainable deep learning, and it didn´t work. The git mentioned an update in TensorFlow that conflicts with SHAP. What do you know about that?

[@yastradamus]

that John Coltrane cover in the back though!

[@moustafa_shomer]

The example based part was kind of shallow, you didn't talk about how they figure out the specific flaws in the model

[@arp_ai]

That tends to be a different problem which can either arise from model but potentially also from the dataset. The problem becomes more model debugging. XAI is one debugging tool, but there are many others, especially deep examinations of the data.

[@abhilashsanap1207]

Some day you should do a video about the background in your videos. Please.

[@RahilQodus]

Thanks a lot. It was a great introduction and really helped me🙏🫀