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  over 3 years ago by awh | Reply

  From what I've read so far I've found some entries in https://blog.piekniewski.info/ quite interesting, e.g.

  https://blog.piekniewski.info/2020/06/08/ai-the-no-bullshit-approach/ (AI - the no bullshit approach) [Posted 10 months ago by Filip Piekniewski]

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From what I've read so far I've found some entries in https://blog.piekniewski.info/ quite interesting, e.g. https://blog.piekniewski.info/2020/06/08/ai-the-no-bullshit-approach/ (AI - the no bullshit approach) [Posted 10 months ago by Filip Piekniewski]
 
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From what I've read so far I've found some entries in https://blog.piekniewski.info/ quite interesting, e.g.
https://blog.piekniewski.info/2020/06/08/ai-the-no-bullshit-approach/ (AI - the no bullshit approach) [Posted 10 months ago by Filip Piekniewski]
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  over 3 years ago by awh | Reply

  Perhaps of interest: https://developers.google.com/machine-learning/crash-course/

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    over 3 years ago by awh | Reply

    So far for the crash-course I just had to ensure installation of numpy, pandas, tensorflow2.

    • 

      over 3 years ago by awh | Reply

      matplotlib

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    Edit Comment 5376
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    matplotlib
     
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    matplotlib
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  Edit Comment 5374
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  So far for the crash-course I just had to ensure installation of numpy, pandas, tensorflow2.
   
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  So far for the crash-course I just had to ensure installation of numpy, pandas, tensorflow2.
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  • 

    over 3 years ago by awh | Reply

    The linear regression exercises at https://developers.google.com/machine-learning/crash-course/first-steps-with-tensorflow/programming-exercises were quite illustrative.

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  The linear regression exercises at https://developers.google.com/machine-learning/crash-course/first-steps-with-tensorflow/programming-exercises were quite illustrative.
   
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  The linear regression exercises at https://developers.google.com/machine-learning/crash-course/first-steps-with-tensorflow/programming-exercises were quite illustrative.
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  • 

    over 3 years ago by awh | Reply

    What makes this course also important is that terminology specific to ML is explained, e.g. one-hot-encoding, feature cross etc.

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  Edit Comment 5468
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  What makes this course also important is that terminology specific to ML is explained, e.g. one-hot-encoding, feature cross etc.
   
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  What makes this course also important is that terminology specific to ML is explained, e.g. one-hot-encoding, feature cross etc.
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Edit Comment 5358
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Perhaps of interest: https://developers.google.com/machine-learning/crash-course/
 
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Perhaps of interest: https://developers.google.com/machine-learning/crash-course/
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  over 3 years ago by awh | Reply

  WRT papers. There are quite some listed in github, e.g.

  https://github.com/rupak-118/AI-papers https://github.com/terryum/awesome-deep-learning-papers https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI

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WRT papers. There are quite some listed in github, e.g. https://github.com/rupak-118/AI-papers https://github.com/terryum/awesome-deep-learning-papers https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI
 
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WRT papers. There are quite some listed in github, e.g.
https://github.com/rupak-118/AI-papers https://github.com/terryum/awesome-deep-learning-papers https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI
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