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** [https://tensorflow.rstudio.com/keras/ R interface to Keras]. I followed the instruction for the installation but got an error of ''illegal operand''. The solution is to use an older version of tensorflow; see [https://github.com/rstudio/tensorflow/issues/228 here]. ''library(keras); install_keras(tensorflow = "1.5")'' (Ubuntu 16.04, Phenom(tm) II X6 1055T) | ** [https://tensorflow.rstudio.com/keras/ R interface to Keras]. I followed the instruction for the installation but got an error of ''illegal operand''. The solution is to use an older version of tensorflow; see [https://github.com/rstudio/tensorflow/issues/228 here]. ''library(keras); install_keras(tensorflow = "1.5")'' (Ubuntu 16.04, Phenom(tm) II X6 1055T) | ||
** https://rviews.rstudio.com/2018/04/03/r-and-tensorflow-presentations/, [https://beta.rstudioconnect.com/ml-with-tensorflow-and-r/#1 Slides] | ** https://rviews.rstudio.com/2018/04/03/r-and-tensorflow-presentations/, [https://beta.rstudioconnect.com/ml-with-tensorflow-and-r/#1 Slides] | ||
* [https://hpc.nih.gov/docs/deep_learning.html Deep Learning on Biowulf] | * [https://hpc.nih.gov/docs/deep_learning.html Deep Learning on Biowulf] | ||
* Raspberry Pi | * Raspberry Pi | ||
| Line 22: | Line 21: | ||
** [https://www.manning.com/books/deep-learning-with-python Deep Learning with Python] by François Chollet, 2017 (available on safaribooksonline) | ** [https://www.manning.com/books/deep-learning-with-python Deep Learning with Python] by François Chollet, 2017 (available on safaribooksonline) | ||
** [https://github.com/janishar/mit-deep-learning-book-pdf Deep Learning] by Ian Goodfellow and Yoshua Bengio and Aaron Courville | ** [https://github.com/janishar/mit-deep-learning-book-pdf Deep Learning] by Ian Goodfellow and Yoshua Bengio and Aaron Courville | ||
* Tensor operations: | |||
** relu(x) = max(0, x) | |||
** Each neural layer from our first network example transforms its input data:'''output = relu(dot(W, input) + b)''' where W and b are the ''weights'' or ''trainable parameters'' of the layer. | |||
* Training process | |||
*# Draw a batch of X and Y | |||
*# Run the network on x (a step called the forward pass) to obtain predictions y_pred. | |||
*# Compute the loss of the network on the batch | |||
*# Update all weights of the network in a way that slightly reduces the loss on this batch. | |||
* Derivative of a tensor operation: the gradient | |||
** Define loss_value = f(W) = dot(W, x) | |||
** W1 = W0 - step * gradient(f)(W0) | |||
* Stochastic gradient descent | |||
* Deep Learning Glossary | * Deep Learning Glossary | ||
** http://www.wildml.com/deep-learning-glossary/ | ** http://www.wildml.com/deep-learning-glossary/ | ||
Revision as of 12:49, 31 December 2018
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- Draw This camera prints crappy drawings of the things you photograph (DIY) with Google's quickdraw.
- What Are Machine Learning Algorithms? Here’s How They Work
- Google的人工智慧開源神器三歲了，它被用在很多你想不到的地方 Nov 2018
TensorFlow
- https://www.tensorflow.org/
- https://tensorflow.rstudio.com/
- R interface to Keras. I followed the instruction for the installation but got an error of illegal operand. The solution is to use an older version of tensorflow; see here. library(keras); install_keras(tensorflow = "1.5") (Ubuntu 16.04, Phenom(tm) II X6 1055T)
- https://rviews.rstudio.com/2018/04/03/r-and-tensorflow-presentations/, Slides
- Deep Learning on Biowulf
- Raspberry Pi
- Books
- Deep Learning with R by François Chollet with J. J. Allaire, 2018. ISBN-10: 161729554X (available on safaribooksonline)
- Deep Learning with Python by François Chollet, 2017 (available on safaribooksonline)
- Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville
- Tensor operations:
- relu(x) = max(0, x)
- Each neural layer from our first network example transforms its input data:output = relu(dot(W, input) + b) where W and b are the weights or trainable parameters of the layer.
- Training process
- Draw a batch of X and Y
- Run the network on x (a step called the forward pass) to obtain predictions y_pred.
- Compute the loss of the network on the batch
- Update all weights of the network in a way that slightly reduces the loss on this batch.
- Derivative of a tensor operation: the gradient
- Define loss_value = f(W) = dot(W, x)
- W1 = W0 - step * gradient(f)(W0)
- Stochastic gradient descent
- Deep Learning Glossary