qubes-thinkpad-x1-extreme-gen3
Files and notes to install/run Qubes 4.1 on a ThinkPad X1 Extreme Gen3 (by dhn)
deeplearning-notes
Notes for Deep Learning Specialization Courses led by Andrew Ng. (by lijqhs)
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| 1 | 71 | |
| 3 | 386 | |
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| over 3 years ago | over 1 year ago | |
| - | MIT License |
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
qubes-thinkpad-x1-extreme-gen3
Posts with mentions or reviews of qubes-thinkpad-x1-extreme-gen3.
We have used some of these posts to build our list of alternatives
and similar projects.
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X1 Extreme gen 3 for Qubes
Will work with 4.1, although still needs some additional workarounds. See some instructions here: https://github.com/dhn/qubes-thinkpad-x1-extreme-gen3/
deeplearning-notes
Posts with mentions or reviews of deeplearning-notes.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-29.
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Intuition for LSTM cell structure
If you want in-depth understanding then I would recommend you to look for Deep Learning Specialization by Andrew Ng. (Course 4). He explained the LSTM and GRU cells in detail (mathematically). You can also find it on YouTube I guess. Hope it helps.
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[D] Best deep learning course?
Best place to get started https://www.coursera.org/specializations/deep-learning
- Which course from deeplearning.ai should I take first? There are so many now
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Where to go from here
I want to expand on what I learnt theory and practice to be able to complete a project, where I can download a video and run it through my model and it will be able highlight specified items, e.g people trees, cars. Will this course help me get there https://www.coursera.org/specializations/deep-learning
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This is my self-learning curriculum for ML. Hope it helps and open to feedback!
Another one from DeepLearning.ai and this is also the most popular course for Deep Learning and Neural Networks - https://www.coursera.org/specializations/deep-learning
- AI roadmap
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Assignments to practice for course "neural-networks-deep-learning"
This course is a part of one of the 5 courses in DL specialization: https://www.coursera.org/specializations/deep-learning. I am taking this course on Coursera where I have finished up to week 2. Now I need to practice for it, but I think I can't access assignments as its locked for paid viewers. Can someone share me the resources for practice or any alternatives you found useful?
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Coursera or Udacity for TF developer certificate
There is another [course] (https://www.coursera.org/specializations/deep-learning) by deeplearning ai that catched my eye and in review they say its more in detail than tf in practice course.
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Career in Computer Vision - Best way to spool up through OMSCS
Deep learning like others said, but I've seen some posts recommending taking an external class, like Andrew Ng's Coursera class https://www.coursera.org/specializations/deep-learning over the GT one. I haven't taken or plan on taking the GT one but some people found it lacking
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How relevant is “A super harsh guide to machine learning” for someone who is just tinkering with machine learning?
My recommendations are worth little, I'm just starting through all this stuff myself. I'm currently taking the Deep Learning specialization on Coursera and trying to map out what else I should be doing.
What are some alternatives?
When comparing qubes-thinkpad-x1-extreme-gen3 and deeplearning-notes you can also consider the following projects:
ideas2 - Another 85+ Ideas for Computing https://samsquire.github.io/ideas2/
coursera-deep-learning-specialization - Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models