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perfopt

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I use ChatGPT to ask questions about my code - including rust lifetimes - and usually get pretty good detailed answers. More recently I started using diesel ORM and was pleasantly surprised that the bot can answer questions about diesel usage.

I learnt Swift several years ago for a App project Haven't really used it since.

My impression is that it is useful only in the Apple ecosystem. It that correct? Is it worth learning for things other than iOS and macOS applications?

This is exactly why banks are unhappy - they have to bear the infra cost. This is also the reason the government is trying to push UPI wallet. I suspect at some future point UPI (non-wallet) transactions will be charged a small fee and wallet transactions will not.

[dead] 3 years ago

Martin Meyerson Berkeley Faculty Research Lecture: Jitendra Malik

OK. I noticed that the images are not accurate when I give my own descriptions. Not sure if this is a limitation of Stable Diffusion. For example, for the text "cat and mouse samurai fight in a forest, watched by a porcupine" I got a cat and a mouse (with a cat's face and tail!!) in a forest sort of fighting. But no - porcupine

Thank you for creating this.

Aah Turbo Pascal. That was the first "big pants" language I learnt and enjoyed.

After that... C++ was fun initially and later felt way too featured. I don't like C++ code now (especially with heavy template use and stl and boost).

I now code in Python (because most of the things I work on are in Python) though I don't like the dynamic typing.

My potential new love is Rust but we are still working out some issues

Thanks for that note. I have an audio classification hobby project (for now). Could you point me to things I should learn to get better at audio classification and generation?

Your comment about time domain making audio difficult - before doing some research I thought it would make it impossible. But looks like people have had some success with using spectrograms of short audio samples. What techniques should I try to learn to deal with the time component of audio?

One idea is to chop up the audio into short samples and treat the resulting images as a video. Then look at DL algorithms that deal with video. Am I on the right track?

Can one do these lessons in any order? For example, do CNN first then jump back to NLP. Or skip the implementation from scratch because I have done a similar one in another course.

This is awesome. One question I have always had - is the research on applying DL for images the most developed compared to other things?

Even DL used for audio processing (classification, separation etc) seems to convert audio to spectral graphs and apply DL to that.

Changing a problem to be expressed as image inputs will be an advantage when using DL as a solution. Would you agree ?

Thank you for creating this course. I started out on Tensor Flow but seeing this material I am in two minds whether I should abandon my TF book and start this one or save it for later. Most likely I am going to dive in :-)