7 votes

In 2016, can $1000.00 buy enough operations per second to be approximately equal to the computational power of a human brain?

The development of CPUs didn't quite keep up with Kurzweil's predictions. But if you also allow for GPUs, his prediction for 2009 is pretty accurate. I think Moore's law slowed down recently and has ...
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7 votes

Can I do deep learning with the 1060 or the 1070 ti?

Regarding specific choices I can't recommend, but if you are completely new, you should probably learn/code some more until you get a GPU. There is a lot to learn in machine learning before GPU ...
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6 votes

What is the reason AMD Radeon is not widely used for machine learning and deep learning?

The main reason that AMD Radeon graphics card is not used for deep learning is not the hardware and raw speed. Instead it is because the software and drivers for deep learning on Radeon GPU is not ...
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  • 1,715
6 votes
Accepted

What size of neural networks can be trained on current consumer grade GPUs? (1060,1070,1080)

Usually the problem is to fit the model into video RAM. If it does not, you cannot train your model at all without big efforts (like training parts of the model separately). If it does, time is your ...
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5 votes

Can I do deep learning with the 1060 or the 1070 ti?

Given that you're a student doing this out of personal interest and wanting to do some gaming on the side, I'd suggest the GTX 1060 6GB since at present the GTX 1070Ti is overpriced due to crypto ...
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  • 291
3 votes
Accepted

What amount of ressources is involved in building an image recognition system?

One answer is infinite amount of time because it can always be better. Another answer is: 10k for training set A PC with a GPU (3~4k USD), google colab (10 USD per month), or other cloud service (...
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3 votes

What size of neural networks can be trained on current consumer grade GPUs? (1060,1070,1080)

As a caveat, I’d suggest that unless you’re pushing up against fundamental technological limits, computation speed and resources should be secondary to design rationale when developing a neural ...
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3 votes

Can I do deep learning with the 1060 or the 1070 ti?

I don't think you need to invest in any kind of GPU unless you're familiar with the computations required for the task you want to achieve using deep learning. Also, by the time you've sufficiently ...
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  • 303
2 votes

In 2016, can $1000.00 buy enough operations per second to be approximately equal to the computational power of a human brain?

Yes, we do have computing systems that do fall in the teraFLOPS range (where 1 teraflop = 1 trillion FLOPS = $10^{12}$ FLOPS) The human brain is a biological system and saying it has some sort of ...
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  • 531
2 votes

What size of neural networks can be trained on current consumer grade GPUs? (1060,1070,1080)

It depends on what you need. You can train any size of network on any resource. The problem is the time of training. If you want to train Inception on an average CPU it will take months to converge. ...
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2 votes

What is the difference between a normal processor and a processor designed for AI?

Specialized AI hardware takes advantage of highly parallelizable nature of many neural network designs. GPUs are designed for pixel crunching - coincidentally very paralelisable too. This is why they ...
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2 votes

What is the difference between a normal processor and a processor designed for AI?

Parallel processing is very important when computing ANNs. Though they are not designed for AIs, GPUs are widely used in machine learning because of their higher capability of paralel processing and ...
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2 votes

What type of reinforcement learning can I do restricted to ~200MB on an average smartphone?

I skimmed through your question and understood that the state/action space is finite, so in this case, RL would be a good option for storage. The most basic RL technique will keep track of a matrix Q ...
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  • 251
1 vote

Does fp32 & fp64 performance of GPU affect deep learning model training?

Deep models are very tolerant to arithmetic underflow. You can hope for neglectable differences in prediction accuracy between FP32 and FP16 models. Check this paper for concrete results.
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  • 469
1 vote

What are the minimum computing resources needed to train a machine learning algorithm?

It might be hard to implement deep reinforcement learning algorithms, especially considering your previous experience and the computing resources you have. They require almost the same (even more) GPU ...
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