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0 Replies and 726 Views
Model Non-Uniqueness and First Principle 726 0
Started by Patrick Ng
In classic model-based inversion, we learn to live with non-unique solution (i.e., there is more than one model that fit the data using criteria like minimum mean squared error). One safeguard is calibration (e.g., seismic-inverted model properties vs. that from well logs or drill bits) to determine if the resulting model makes sense or not. Likewise, we can have different neural network configurations (e.g., number of hidden layers and neurons within each layer, even different activation fun...
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17 Nov 2017 11:18 AM |
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0 Replies and 617 Views
TensorFlow in the Cloud - AWS, GCP or Azure? 617 0
Started by Patrick Ng
TensorFlow on three platforms, first impression - Amazon Web Services (AWS) - does require use of Unix script, and if running from Windows, additional download of utility PuTTY for ssh connections (geek speak for cybersecurity key). ttps://www.datacamp.com/community/tutorials/deep-learning-jupyter-aws Google Cloud Platform (GCP) - much the same as AWS, and no PuTTY download required. https://towardsdatascience.com/running-jupyter-notebook-in-google-cloud-platform-in-15-min-61e16da34d52 ...
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16 Nov 2017 03:22 PM |
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1 Replies and 619 Views
First Principle - Model 619 1
Started by Patrick Ng
Recall what 'model' meant and lessons learnt: Amplitude-versus-Offset (AVO) Intercept and slope (gradient) multiplied by sine square of angle in a linearised model Product of intercept and slope is an attribute (e.g., feature engineering) Intercept and slope are two distinct features Inversion Gulf coast - low-frequency trend / young clastic sediments / cyclic deposition North Sea - discrete-structure layers / carbonates / unconformities Prestack Depth Migration GOM - vel...
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3 Replies and 1312 Views
Tensor Processing Unit (TPU) 1312 3
Started by Patrick Ng
The first generation TPU was only able to handle inference. The new one can also be used for training machine learning models, a significant part of the machine learning workflow all within this single, powerful chip. That means that you can build a machine learning model — for example, to correctly identify an object in a photo is a tree, a car or a cat. Inference in machine learning refers to the statistical likelihood that the machine’s conclusions are correct — for example, based on the m...
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0 Replies and 697 Views
First Principle and Neural Network Implementation 697 0
Started by Patrick Ng
This thread aims at linking first principle to deeper understanding of neural network implementation. First principle first, in both Unconventional and classic producing area, typical IP distribution is anything but a bell curve. For example, http://aemstatic-ww2.azureedge.net/content/dam/ogfj/print-articles/volume-13/issue-12/1612OGFJng-z01.jpg.scale.LARGE.jpg Recall geology fundamental, grain size distribution of sediments is also log normal. It suffices to reason that using...
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02 Oct 2017 11:11 PM |
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