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skip links skip to primary navigation skip to content skip to primary sidebar tim dettmers making deep learning accessible. header right blog posts topics deep learning (5) hardware (6) neuroscience (1) main navigation blog deep learning hardware neuroscience publications about me which gpu(s) to get for deep learning: my experience and advice for using gpus in deep learning 2017-04-09 by tim dettmers 714 comments deep learning is a field with intense computational requirements and the choice of your gpu will fundamentally determine your deep learning experience. with no gpu this might look like months of waiting for an experiment to finish, or running an experiment for a day or more only to see that the chosen parameters were off. with a good, solid gpu, one can quickly iterate over deep learning networks, and run experiments in days instead of months, hours instead of days, minutes instead of hours. so making the right choice when it comes to buying a gpu is critical. so how do you select the gpu which is right for you? this blog post will delve into that question and will lend you advice which will help you to make choice that is right for you. [read more…] about which gpu(s) to get for deep learning: my experience and advice for using gpus in deep learning filed under: deep learning , hardware tagged with: cuda , deep learning , gpu , neural networks deep learning hardware limbo 2017-12-21 by tim dettmers 87 comments with the release of the titan v, we now entered deep learning hardware limbo. it is unclear if nvidia will be able to keep its spot as the main deep learning hardware vendor in 2018 and both amd and intel nervana will have a shot at overtaking nvidia. so for consumers, i cannot recommend buying any hardware right now. the most prudent choice is to wait until the hardware limbo passes. this might take as little as 3 months or as long as 9 months. so why did we enter deep learning hardware limbo just now? [read more…] about deep learning hardware limbo filed under: deep learning , hardware tagged with: amd , deep learning , gpus , hardware , intel , nervana , nvidia credit assignment in deep learning 2017-09-16 by tim dettmers 15 comments this morning i got an email about my blog post discussing the history of deep learning which rattled me back into a time of my academic career which i rather not think about. it was a low point which nearly ended my master studies at the university of lugano, and it made me feel so bad about blogging that i took two long years to recover. so what has happened? [read more…] about credit assignment in deep learning filed under: deep learning tagged with: deep learning , philosophy deep learning research directions: computational efficiency 2017-08-31 by tim dettmers 15 comments this blog post looks at the growth of computation, data, deep learning researcher demographics to show that the field of deep learning could stagnate over slowing growth. we will look at recent deep learning research papers which strike up similar problems but also demonstrate how one could to solve these problems. after discussion of these papers, i conclude with promising research directions which face these challenges head on. [read more…] about deep learning research directions: computational efficiency filed under: deep learning tagged with: ai , computational growth , deep learning , future , research directions the brain vs deep learning part i: computational complexity — or why the singularity is nowhere near 2015-07-27 by tim dettmers 176 comments in this blog post i will delve into the brain and explain its basic information processing machinery and compare it to deep learning. i do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. thereby we will see that a neuron and a convolutional net are very similar information processing machines. while performing this comparison, i will also discuss the computational complexity of these processes and thus derive an estimate for the brains overall computational power. i will use these estimates, along with knowledge from high performance computing, to show that it is unlikely that there will be a technological singularity in this century. [read more…] about the brain vs deep learning part i: computational complexity — or why the singularity is nowhere near filed under: neuroscience tagged with: deep learning , dendritic spikes , high performance computing , neuroscience , singularity understanding convolution in deep learning 2015-03-26 by tim dettmers 105 comments convolution is probably the most important concept in deep learning right now. it was convolution and convolutional nets that catapulted deep learning to the forefront of almost any machine learning task there is. but what makes convolution so powerful? how does it work? in this blog post i will explain convolution and relate it to other concepts that will help you to understand convolution thoroughly. [read more…] about understanding convolution in deep learning filed under: deep learning tagged with: convolution , convolution kernel , convolutional nets , deep learning a full hardware guide to deep learning 2015-03-09 by tim dettmers 620 comments deep learning is very computationally intensive, so you will need a fast cpu with many cores, right? or is it maybe wasteful to buy a fast cpu? one of the worst things you can do when building a deep learning system is to waste money on hardware that is unnecessary. here i will guide you step by step through the hardware you will need for a cheap high performance system. [read more…] about a full hardware guide to deep learning filed under: hardware tagged with: cpu , deep learning , hardware , machine learning , pci , ram , ssd how to parallelize deep learning on gpus part 2/2: model parallelism 2014-11-09 by tim dettmers 21 comments in my last blog post i explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning . in this blog post i will focus on model parallelism. [read more…] about how to parallelize deep learning on gpus part 2/2: model parallelism filed under: hardware tagged with: cuda , deep learning , gpu , hpc , neural networks , parallel computing how to parallelize deep learning on gpus part 1/2: data parallelism 2014-10-09 by tim dettmers 17 comments in my last blog post i showed what to look out for when you build a gpu cluster. most importantly, you want a fast network connection between your servers and using mpi in your programming will make things much easier than to use the options available in cuda itself. in this blog post i explain how to utilize such a cluster to parallelize neural networks in different ways and what the advantages and downfalls are for such algorithms. the two different algorithms are data and model parallelism. in this blog entry i will focus on data parallelism. [read more…] about how to parallelize deep learning on gpus part 1/2: data parallelism filed under: hardware tagged with: cuda , deep learning , gpu , hpc , mpi , neural networks how to build and use a multi gpu system for deep learning 2014-09-21 by tim dettmers 129 comments when i started using gpus for deep learning my deep learning skills improved quickly. when you can run experiments of algorithms and algorithms with different parameters and gain rapid feedback you can just learn much more quickly. at the beginning, deep learning is a lot of trial and error: you have to get a feel what parameters need to be adjusted, or what puzzle piece is missing in order to get a good result. a gpu helps you to fail quickly and learn important lessons so that you can keep improving. soon my deep learning skills were sufficient to take the 2 nd place in the crowdflower competition where the task was to predict weather labels from given tweets (sunny, raining etc.). after this success i was tempted

URL analysis for timdettmers.com

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http://timdettmers.com/2015/07/27/brain-vs-deep-learning-singularity/#comments
http://timdettmers.com/tag/neuroscience-2/
http://timdettmers.com/2014/11/09/model-parallelism-deep-learning/
http://timdettmers.com/2015/07/27/brain-vs-deep-learning-singularity/
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http://timdettmers.com/2014/09/21/how-to-build-and-use-a-multi-gpu-system-for-deep-learning/#more-52
http://timdettmers.com/2015/03/09/deep-learning-hardware-guide/#comments

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