[FRIAM] Deep learning training material

[glen]

Yes, the money/expertise bar is still pretty high. But TANSTAAFL still applies. And the overwhelming evidence is coming in that specific models do better than those trained up on diverse data sets, "better" meaning less prone to subtle bullsh¡t. What I find fascinating is tools like OpenAI *facilitate* trespassing. We have a wonderful bloom of non-experts claiming they understand things like "deep learning". But do they? An old internet meme is brought to mind: "Do you even Linear Algebra, bro?" >8^D

On 1/8/23 01:06, Jochen Fromm wrote:
> I have finished a number of Coursera courses recently, including "Deep Learning & Neural Networks with Keras" which was ok but not great. The problems with deep learning are
> 
> * to achieve impressive results like chatGPT from OpenAi or LaMDA from Goggle you need to spend millions on hardware
> * only big organisations can afford to create such expensive models
> * the resulting network is s black box and it is unclear why it works the way it does
> 
> In the end it is just the same old back propagation that has been known for decades, just on more computers and trained on more data. Peter Norvig calls it "The unreasonable effectiveness of data"
> https://research.google.com/pubs/archive/35179.pdf
> 
> -J.
> 
> 
> -------- Original message --------
> From: Russ Abbott <russ.abbott at gmail.com>
> Date: 1/8/23 12:20 AM (GMT+01:00)
> To: The Friday Morning Applied Complexity Coffee Group <friam at redfish.com>
> Subject: Re: [FRIAM] Deep learning training material
> 
> Hi Pieter,
> 
> A few comments.
> 
>   * Much of the actual deep learning material looks like it came from the Kaggle "Deep Learning <https://www.kaggle.com/learn/intro-to-deep-learning>" sequence.
>   * In my opinion, R is an ugly and /ad hoc/ language. I'd stick to Python.
>   * More importantly, I would put the How-to-use-Python stuff into a preliminary class. Assume your audience knows how to use Python and focus on Deep Learning. Given that, there is only a minimal amount of information about Deep Learning in the write-up. If I were to attend the workshop and thought I would be learning about Deep Learning, I would be disappointed--at least with what's covered in the write-up.
> 
>     I say this because I've been looking for a good intro to Deep Learning. Even though I taught Computer Science for many years, and am now retired, I avoided Deep Learning because it was so non-symbolic. My focus has always been on symbolic computing. But Deep Learning has produced so many extraordinarily impressive results, I decided I should learn more about it. I haven't found any really good material. If you are interested, I'd be more than happy to work with you on developing some introductory Deep Learning material. 
> 
> -- Russ Abbott
> Professor Emeritus, Computer Science
> California State University, Los Angeles
> 
> 
> On Thu, Jan 5, 2023 at 11:31 AM Pieter Steenekamp <pieters at randcontrols.co.za <mailto:pieters at randcontrols.co.za>> wrote:
> 
>     Thanks to the kind support of OpenAI's chatGPT, I am in the process of gathering materials for a comprehensive and hands-on deep learning workshop. Although it is still a work in progress, I welcome any interested parties to take a look and provide their valuable input. Thank you!
> 
>     You can get it from:
>     https://www.dropbox.com/s/eyx4iumb0439wlx/deep%20learning%20training%20rev%2005012023.zip?dl=0 <https://www.dropbox.com/s/eyx4iumb0439wlx/deep%20learning%20training%20rev%2005012023.zip?dl=0>
> 

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