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. 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).

Thanks in advance for your input. 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.

Introducing high-performance neural network framework with both CPU and GPU training support. Enterprise Mathematica; Wolfram. Neural Networks. Pearson offers special pricing when you package your text with other student resources. If you're interested in creating a cost-saving package for your students.

My contact info can be found at the product's homepage on the Wolfram web. Improvement suggestions are very welcome.

Scott Scba Serial Number Location here. Free Hack Gmail Password Online. 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.

About Features Features • Teaches the reader about what neural networks are, and how to manipulate them within the Mathematica environment. Originlab 8 Serial Key. • Shows how Mathematica can be used to implement and experiment with neural network architectures. • Addresses a major topic related to neural networks in each chapter, or a specific type of neural network architecture.

• Contains exercises, suggested projects, and supplementary reading lists with each chapter. • Includes Mathematica application programs ('packages') in Appendix. (Also available electronically from MathSource.). Description • Copyright 1994 • Dimensions: 6-1/4x9-1/4 • Pages: 352 • Edition: 1st • Book • ISBN-10: 0-201-56629-X • ISBN-13: 978-0-201-56629-1 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. For students of neural networks in upper-level undergraduate or beginning graduate courses in computer science, engineering, and related areas. Also for researchers and practitioners interested in using Mathematica as a research tool. Features • Teaches the reader about what neural networks are, and how to manipulate them within the Mathematica environment. • Shows how Mathematica can be used to implement and experiment with neural network architectures.