All 7 entries tagged Tensorflow

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March 05, 2020

list of submitted projects and information about late submission penalties

Follow-up to Submission date and instructions for your project work from Rudo's blog

Dear all,

thus far I have received submissions from (10/02/30 10:00)

  1. Jettawat Khantaveramongkol
  2. Juan Roman Roche
  3. Hendrik Schlueter
  4. Alexios Malaquias
  5. DanielPepin Fowan
  6. Burak Civitcioglu
  7. Roman Perrier (one day late)
  8. Jeremy Cadran (two days late)

Please note that I intend to apply late submission penalties, following roughly similar Warwick penalties which penalize a late submission with a 5% reduction in final points for every day late. A late day starts in the first minute after the dealine and the time of submission follows from the time of the last upload.



March 02, 2020

PS: don't worry too much about maximizing epochs

Follow-up to Submission date and instructions for your project work from Rudo's blog

Just to add: do not worry too much about running your code to the maximum of epochs. It is clear to us that most people do not have access to state of the art graphics cards or hundreds of processors. What is more important is to see that you came up with a good idea where machine learning can be used, executed the right commands to get some training and analysis done, and found the right data to train on. In fact, we migth even try to run some of your code for more epochs to see how results can get better!

Submission date and instructions for your project work

Follow-up to teaching schedule, now ready from Rudo's blog

Dear all,

you should now all be close to finishing your project work for the machine learning course. I hope it has all gone well and that you had some fun exploring machine learning. The official date for submission is

Friday, March 6 at 17:00.

For submission, please remember that we want you to submit a running machine learning code using a jupyter notebook with comments that explain step by step what you did and why. Please also supply the data so that Djena and I can run the code ourselves. The easiest way to send us the submission is via

where you can upload both jupyter notebook and data (preferably as a single .zip file). If your data files are too big for upload there (I have 4.5GB in total on that upload drive), please send a link at which we can then download the data. Please remember that it is your responsibility to make sure data and notebook work "out of the box", i.e. without us having to change anything before they work, including directories.

We aim to have feedback for you by the end of March and will communicate this by email to each of you (so please also let me have your preferred email address in your jupyter notebook).


December 12, 2019

teaching schedule, now ready

Follow-up to Machine Learning – a hands–on course from Rudo's blog

Dear all,

the schedule of the course has now become clearer: we continue to teach every Monday 0930-1230 in room E4.28 (except for one day in January and one day in February) for 10 weeks overall. For the 1st 5 weeks (with 4 more weeks to come), I will actually "teach", i.e. with prepared lectures for 2 hours 0930-1130, while Djenabou will be available for 1 hr more 1130-1230 to help you with the exercises. Then in the last 5 weeks, Djenabou will be available 0930-1230 to help with your project work (she won't do it for you, but might be able to help with technical problems).

A full schedule should be available soon.

See you next Monday!


December 09, 2019

Thoughts on the 1st lecture session

Writing about web page /rroemer/entry/installing_your_own/

Writing about an entry you don't have permission to view

Dear all,

[1] today in the lecture, we saw that it was not entirely simple to install your own ML machine with Tensorflow/Keras and FastAI. I have now made a blog entry on how to do this. You can find it here.

[2] In addition, when running the FastAI code, we saw that it might take a long time even for just the pets categorization. I just ran the pets notebook (please download a new version) and have made the pets-stage-1 and pets-stage-1-50 trained models available on the download area. You need to move these models into where FastAI keeps them which in my set-up is


Just copy the twp .pth files there and you should be able to run the pets code after the


fastai commands in the notebook.

[3] When you "play" with the notebook called "FastAI-lesson2-download.ipynb", note that the file contains the cats/teddys/wolves that I managed to download and use for the exercise.

[4] Some of you argued that your own machines may not be able to do meaningful ML as you do not have a GPU. Have a look at what the FastAI guys have collected in terms of possible free GPU instances, i.e. Using a GPU. I have not yet used any of these services, but would be keen to learn from your experiences!

OK, keep checking this blog for up-to-date information before we meet again next week.

December 06, 2019

Things to do before the 1st lectures

Follow-up to Machine Learning – a hands–on course from Rudo's blog

Dear all,

[1] some of you will have registered for the short Machine Learning (ML) course that we are planning to give, starting next week. ML is best "learned" by doing it, so we are planning to use the computer room E4.28 for exercises during all the lectures. But we think you might also find it more useful if you were to bring your laptops along and install+run the ML codes there.

[2] We are expecting to run all the ML on a Ubuntu installation (Francois Chollet, one of the key people in the field, says "it’s possible to use Windows, too, but I don’t recommend it"). Other Linux installations should also work. If you only have Windows, Rudo recommends installing


with a Ubuntu 19.10 installation (

[3] During the course, we intend to offer you downloadable ML source codes via Rudo's blog at

where you can also see a copy of this email. The direct download link is in the first entry of the blog and given here as well, i.e.

[4] We want to share literature on ML with you using Mendeley, i.e. Although the Mendeley Desktop might be convenient to install (we have it on our machines), the online interface could be ok for the course. But in order to "share" PDFs with you, you need to have a (free) Mendeley account. Please create one.

[5] Last, we, Djena and Rudo, are both new to ML, so this will be a course where we will all explore ML together! We hope you'll enjoy it.

- Rudo and Djena

December 05, 2019

Machine Learning – a hands–on course

Dear all,

for this course, you should be able to find all course material for download at

Please let me know in case this does not work!


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  • Please note that this link to download is now obsolete. Attendees for the 2021 MPAGS/CYU course shou… by Rudo Roemer on this entry
  • To load Tensorflow and PyTorch (and Keras) modules on ORAC may you may have to module load a few mor… by Daniel Paget on this entry
  • That's cool. Perhaps you want to show people during class next week how to run on Kaggle? by Rudo Roemer on this entry
  • Regarding possible alternatives to benefit form a GPU, I have personally used Kaggle, by Google, its… by Juan on this entry
  • Hello to you all, I was just trying to make the programs run in my VirtualBox with Ubuntu 18.04 inst… by Malaquias Correa on this entry

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