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Readers will learn how to simulate neural network operations using Mathematica, and will learn techniques for employing Mathematica to assess neural network behavior and performance. For students of neural networks in upper-level undergraduate or beginning graduate courses in computer science, engineering, and related areas. This book introduces neural networks, their operation, and application, in the context of the interactive Mathematica environment. Readers will learn how to simulate neural network operations using Mathematica, and will learn techniques for employing Mathematica to assess neural network behavior and performance.
- Simulating Neural Networks With Mathematica Warehouse
- Simulating Neural Networks With Mathematica Warehouse
I just created a VERY large neural net, albeit on very powerful hardware, and imagine my shock and disappointment, when I realized that NeuralFit[] from NeuralNetworks` package only seems to use one core, and not even to its fullest capacity. I was heartbroken. Do I really have to write an entire NN implementation from scratch? Or did I miss something simple?
My net took 200 inputs to 2 hidden layers of 300 neurons to produce 100 outputs. I understand we're talking about trillions of calculations, but as long as I know my hardware is the weak point - that can be upgraded. Program was terminated by signal 11 in oracle reports. It should handle training of such a net fairly well if left alone for a while (4Ghz 8-thread machine with 24Gb of 2000Mhz CL7 memory running RAID-0 SSD drives on SATA-III - I'm fairly sure).
Ideas? Suggestions? https://everspectrum800.weebly.com/blog/download-photos-from-itouch-to-mac. Thanks in advance for your input.
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Gregory KlopperGregory Klopper3 Answers
You can contact the author of the package directly, he is a very approachable fellow and might be able to make some suggestions.
Simulating Neural Networks With Mathematica Warehouse
I am the author of the Neural Network Package. It is easy to parallelize the evaluation of a neural network given the input. That is, to compute the output of the network given the inputs (and all the weights, the parameters of the network). However, this evaluation is not very time consuming and it is not very interesting to parallellize it for most problems. On the other hand, the training of the network is often time consuming and, unfortunately, not easy to parallelize. The training can be done with a different algorithms and best ones are not easy to parallelize. My contact info can be found at the product's homepage on the Wolfram web. Improvement suggestions are very welcome.
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On Mac OS X Yosemite the green icon in every window: is by default used to make the application full screen. Another way to approach this problem is to ignore the maximize button entirely and to install the open source app spectacle which offers the keyboard shortcut. Cannot maximize a window by simply clicking on the Photoshop CC app icon. Mac os x apps. According to Mac Dock documentation: 'The Dock keeps applications on its left side, while Stacks and minimized windows are kept on its right.If you look closely, you'll see a vertical separator line that separates them.' Meaning, once the application is minimized, you have a NEW dock icon in the right side, and you need to click on it in order to maximize your application. Longtime Windows users who have migrated to Mac OS X may require a little time to get used to Apple’s operating system. There’s a button to maximize Safari to full-screen, but there are many others apps — Finder, for instance — that cannot be resized using the zoom button.
The last version of the package works fine one version 9 and 10 if you switch off the suggestion bar (under preferences). The reason for that is that the package use the old HelpBrowser for the documentation and it crash in combination with the suggestion bar.
yours Jonas
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Voxengo span plus. I'm not sure how you wrote the code or how it is written inside the package you are using; try to use vectorization, it really speeds up the linear algebra computations. In the ml-class.org course you can see how it's made.
lmsasulmsasuSimulating Neural Networks With Mathematica Warehouse
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