際際滷shows by User: janachakkra / http://www.slideshare.net/images/logo.gif 際際滷shows by User: janachakkra / Wed, 30 Apr 2014 12:53:13 GMT 際際滷Share feed for 際際滷shows by User: janachakkra Parallel Machine Learning- DSGD and SystemML /slideshow/bdasem-parallel-machine-learning/34137835 bdasem-parallelmachinelearning-140430125313-phpapp01
]]>
]]> Wed, 30 Apr 2014 12:53:13 GMT /slideshow/bdasem-parallel-machine-learning/34137835 janachakkra@slideshare.net(janachakkra) Parallel Machine Learning- DSGD and SystemML janachakkra <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bdasem-parallelmachinelearning-140430125313-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Parallel Machine Learning- DSGD and SystemML from Janani C
]]> 1939 6 https://cdn.slidesharecdn.com/ss_thumbnails/bdasem-parallelmachinelearning-140430125313-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Parallel Machine Learning /janachakkra/parallel-machine-learning parallelmachinelearningv1-140224085533-phpapp01
Scalability has been an essential factor for any kind of computational algorithm while considering its performance. In this Big Data era, gathering of large amounts of data is becoming easy. Data analysis on Big Data is not feasible using the existing Machine Learning (ML) algorithms and it perceives them to perform poorly. This is due to the fact that the computational logic for these algorithms is previously designed in sequential way. MapReduce becomes the solution for handling billions of data efficiently. In this report we discuss the basic building block for the computations behind ML algorithms, two different attempts to parallelize machine learning algorithms using MapReduce and a brief description on the overhead in parallelization of ML algorithms.]]>
Scalability has been an essential factor for any kind of computational algorithm while considering its performance. In this Big Data era, gathering of large amounts of data is becoming easy. Data analysis on Big Data is not feasible using the existing Machine Learning (ML) algorithms and it perceives them to perform poorly. This is due to the fact that the computational logic for these algorithms is previously designed in sequential way. MapReduce becomes the solution for handling billions of data efficiently. In this report we discuss the basic building block for the computations behind ML algorithms, two different attempts to parallelize machine learning algorithms using MapReduce and a brief description on the overhead in parallelization of ML algorithms.]]> Mon, 24 Feb 2014 08:55:32 GMT /janachakkra/parallel-machine-learning janachakkra@slideshare.net(janachakkra) Parallel Machine Learning janachakkra Scalability has been an essential factor for any kind of computational algorithm while considering its performance. In this Big Data era, gathering of large amounts of data is becoming easy. Data analysis on Big Data is not feasible using the existing Machine Learning (ML) algorithms and it perceives them to perform poorly. This is due to the fact that the computational logic for these algorithms is previously designed in sequential way. MapReduce becomes the solution for handling billions of data efficiently. In this report we discuss the basic building block for the computations behind ML algorithms, two different attempts to parallelize machine learning algorithms using MapReduce and a brief description on the overhead in parallelization of ML algorithms. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/parallelmachinelearningv1-140224085533-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Scalability has been an essential factor for any kind of computational algorithm while considering its performance. In this Big Data era, gathering of large amounts of data is becoming easy. Data analysis on Big Data is not feasible using the existing Machine Learning (ML) algorithms and it perceives them to perform poorly. This is due to the fact that the computational logic for these algorithms is previously designed in sequential way. MapReduce becomes the solution for handling billions of data efficiently. In this report we discuss the basic building block for the computations behind ML algorithms, two different attempts to parallelize machine learning algorithms using MapReduce and a brief description on the overhead in parallelization of ML algorithms.
Parallel Machine Learning from Janani C
]]> 841 7 https://cdn.slidesharecdn.com/ss_thumbnails/parallelmachinelearningv1-140224085533-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/bdasem-parallelmachinelearning-140430125313-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/bdasem-parallel-machine-learning/34137835 Parallel Machine Learn... https://cdn.slidesharecdn.com/ss_thumbnails/parallelmachinelearningv1-140224085533-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds janachakkra/parallel-machine-learning Parallel Machine Learning