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Neural Network Architectures: Determining the Number of Hidden Nodes

Neural Network Architectures: Determining the Number of Hidden Nodes

Figuring Out the Number of Hidden Nodes: Then and Now   One of the most demanding questions in developing neural networks (of any size or complexity) is determining the architecture: number of layers, nodes-per-layer, and other factors. This was an important question in the late 1980’s and early 1990’s, when neural networks first emerged. Deciding on the network architecture details is even more challenging today. In this post, we’re going to look at some strategies for deciding on the number…

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Labor Day Reading and Academic Year Kick-Off

Labor Day Reading and Academic Year Kick-Off

Deep Learning / Machine Learning Reading and Study Guide:   Several of you have been asking for guided reading lists. This makes sense.   Your Starting Point for Neural Networks, Deep Learning, and Machine Learning   Your study program (reading and code) depends on where you are. Starting out (High-grass country; St. Louis to Alcove Springs): Basic neural networks and deep learning; architecture for common networks, such as CNNs (convolutional neural networks); learning rules and architecture design. Well on the…

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Big Data, Big Graphs, and Graph Theory: Tools and Methods

Big Data, Big Graphs, and Graph Theory: Tools and Methods

Big Graphs Need Specialized Data Storage and Computational Methods {A Working Blogpost – Notes for research & study} Processing large-scale graph data: A guide to current technology, by Sherif Sakr (ssakr@cse.unsw.edu.au), IBM Developer Works (10 June 2013). Note: Dr. Sherif Sakr is a senior research scientist in the Software Systems Group at National ICT Australia (NICTA), Sydney, Australia. He is also a conjoint senior lecturer in the School of Computer Science and Engineering at University of New South Wales. He…

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GPUs, CPUs, MIPS, and Brain-Based Computation

GPUs, CPUs, MIPS, and Brain-Based Computation

GPUs, CPUs, MIPS, and Brain-Based Computation Quick links to useful diagrams: Michael Galloy has produced a good chart showing increase in GPU vs CPU processing over this past decade; nicely continues the line of thought about nonlinear increases in processing power. Look at: http://michaelgalloy.com/2013/06/11/cpu-vs-gpu-performance.html See also post by Karl Rupp: http://www.karlrupp.net/2013/06/cpu-gpu-and-mic-hardware-characteristics-over-time/ Also, this post by NVIDIA: http://http.developer.nvidia.com/GPUGems2/gpugems2_chapter29.html For detailed discussion (including appropriate algorithms/methods), but NOT figures, see: http://pcl.intel-research.net/publications/isca319-lee.pdf Debunking the 100X GPU vs. CPU Myth: An Evaluation of Throughput Computing…

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Nonextensive Statistical Mechanics – Good Read on Advanced Entropy Formulation

Nonextensive Statistical Mechanics – Good Read on Advanced Entropy Formulation

Advances in Thinking about Entropy Starting through Tsallis’s book on entropy; Introduction to Nonextensive Statistical Mechanics. This is a fascinating discussion – really, it’s the roots of philosophy; the real “what-is-so” about the world. Which minimally requires a good solid year or two of graduate-level statistical thermodynamics to even start the read. But worth it. There’s some potential applications of this approach to areas in which I’ve worked before; need to mull this over and jig some ideas about to…

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Accelerating Change – A Good Read

Accelerating Change – A Good Read

Writing to you within hours of summer solstice, 2010 – we now have 2 1/2 years (approximately) to the time that has been targeted by multiple cultures as a “pivot point” in human experience. The idea that we are accelerating in our experience on this planet is not new. Right now, this idea is receiving a great deal of attention – too much of which is “acceleration” of emotional content, and not an objective assessment. In this sense, John Smart’s…

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Non-Equilibrium Information Theory (DARPA group)

Non-Equilibrium Information Theory (DARPA group)

Of possible interest — DARPA group attempting to use non-equilibrium information theory to study mobile ad hoc wireless networks (MANETs). Lots of information theory pubs, not too sure yet they’re really on to what constitutes “non-equilibrium,” worth investigating.