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Machine Learning methods suited for CPU


Research in high-dimensional statistics vs. machine learning?Classification in noiseIID violation in machine learningBoolean classification on stringsApplied statistics in Machine Learning, AI, Neural NetworksBest ML technique to suggest predictor variablesIs Java or Python a better choice for an application involving data intensive algorithms employing natural language processing?Are there any frameworks available that allow for automated large scale supervised machine learning?Fast introduction to deep learning in Python, with advanced math and some machine learning backgrounds, but not much Python experienceAdvice on what Machine Learning Algorithms to study for a Job to candidate matching algorithm













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$begingroup$


I have a large number of x86_64 cores available to me, but no GPUs or TPUs. Which Machine Learning techniques are suited for execution on a CPU? I would imagine more "statistical learning" techniques rather than "deep learning".



The reason I ask is that I'm interesting in studying some state-of-the art techniques, but I'm in the situation noted above.










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    0












    $begingroup$


    I have a large number of x86_64 cores available to me, but no GPUs or TPUs. Which Machine Learning techniques are suited for execution on a CPU? I would imagine more "statistical learning" techniques rather than "deep learning".



    The reason I ask is that I'm interesting in studying some state-of-the art techniques, but I'm in the situation noted above.










    share|improve this question







    New contributor




    OregonTrail is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$














      0












      0








      0





      $begingroup$


      I have a large number of x86_64 cores available to me, but no GPUs or TPUs. Which Machine Learning techniques are suited for execution on a CPU? I would imagine more "statistical learning" techniques rather than "deep learning".



      The reason I ask is that I'm interesting in studying some state-of-the art techniques, but I'm in the situation noted above.










      share|improve this question







      New contributor




      OregonTrail is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I have a large number of x86_64 cores available to me, but no GPUs or TPUs. Which Machine Learning techniques are suited for execution on a CPU? I would imagine more "statistical learning" techniques rather than "deep learning".



      The reason I ask is that I'm interesting in studying some state-of-the art techniques, but I'm in the situation noted above.







      machine-learning hardware






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      share|improve this question







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      asked 2 days ago









      OregonTrailOregonTrail

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          With 12 or more CPU cores, you wan work with pretty much any deep learning model. Only thing that will not work is training with 100k+ images or more than TBs of test. For such scenarios; you can seed the model with transfer learning.



          For example, training a Cat/Dog image classification model (from scratch) takes 2 hours on 6 core i5 (as compared to 30 minutes on GTX 1080ti).






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            $begingroup$

            With 12 or more CPU cores, you wan work with pretty much any deep learning model. Only thing that will not work is training with 100k+ images or more than TBs of test. For such scenarios; you can seed the model with transfer learning.



            For example, training a Cat/Dog image classification model (from scratch) takes 2 hours on 6 core i5 (as compared to 30 minutes on GTX 1080ti).






            share|improve this answer









            $endgroup$

















              1












              $begingroup$

              With 12 or more CPU cores, you wan work with pretty much any deep learning model. Only thing that will not work is training with 100k+ images or more than TBs of test. For such scenarios; you can seed the model with transfer learning.



              For example, training a Cat/Dog image classification model (from scratch) takes 2 hours on 6 core i5 (as compared to 30 minutes on GTX 1080ti).






              share|improve this answer









              $endgroup$















                1












                1








                1





                $begingroup$

                With 12 or more CPU cores, you wan work with pretty much any deep learning model. Only thing that will not work is training with 100k+ images or more than TBs of test. For such scenarios; you can seed the model with transfer learning.



                For example, training a Cat/Dog image classification model (from scratch) takes 2 hours on 6 core i5 (as compared to 30 minutes on GTX 1080ti).






                share|improve this answer









                $endgroup$



                With 12 or more CPU cores, you wan work with pretty much any deep learning model. Only thing that will not work is training with 100k+ images or more than TBs of test. For such scenarios; you can seed the model with transfer learning.



                For example, training a Cat/Dog image classification model (from scratch) takes 2 hours on 6 core i5 (as compared to 30 minutes on GTX 1080ti).







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 2 days ago









                Shamit VermaShamit Verma

                90929




                90929




















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