AI
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Applications
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- This New App Is Like Shazam for Your Nature Photos. Seek App.
- 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
- https://hub.docker.com/r/andrie/tensorflowr/ (outdated)
- 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
- Deep Learning Glossary
Keras
- Derivative of a tensor operation: the gradient
- Define loss_value = f(W) = dot(W, x)
- W1 = W0 - step * gradient(f)(W0)
- Stochastic gradient descent
- 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.
Keras (in order to use Keras, you need to install TensorFlow or CNTK or Theano):
- Define your training data: input tensors and target tensors.
- Define a network of layers (or model). Two ways to define a model:
- using the keras_model_sequential() function (only for linear stacks of layers, which is the most common network architecture by far) or
model <- keras_model_sequential() %>% layer_dense(units = 32, input_shape = c(784)) %>% layer_dense(units = 10, activation = "softmax")
- the functional API (for directed acyclic graphs of layers, which let you build completely arbitrary architectures)
input_tensor <- layer_input(shape = c(784)) output_tensor <- input_tensor %>% layer_dense(units = 32, activation = "relu") %>% layer_dense(units = 10, activation = "softmax") model <- keras_model(inputs = input_tensor, outputs = output_tensor)
- using the keras_model_sequential() function (only for linear stacks of layers, which is the most common network architecture by far) or
- Compile the learning process by choosing a loss function, an optimizer, and some metrics to monitor.
model %>% compile( optimizer = optimizer_rmsprop(lr = 0.0001), loss = "mse", metrics = c("accuracy") ) - Iterate on your training data by calling the fit() method of your model.
model %>% fit(input_tensor, target_tensor, batch_size = 128, epochs = 10)
The following examples can be found at R Markdown Notebooks for "Deep Learning with R"