Denoise with Auto Encoder of H2O in Python for MNIST

We talked about auto-encoder here and here with R (https://charleshsliao.wordpress.com/2017/04/14/identify-arguments-of-h2o-deep-learning-model-with-tuned-auto-encoder-in-r-with-mnist/). We also talked about the three functions of auto encoder above. This is a pretty standard example used for benchmarking anomaly detection models. We use Python3 and H2O framework to build auto-encoder. More details can be found in Sebastian Raschka's book: https://www.goodreads.com/book/show/25545994-python-machine-learning?ac=1&from_search=true

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RNN in TensorFlow in Python&R, with MNIST

Though it is more convenient to conduct TensorFlow framework in python, we also talked about how to apply Tensorflow in R here:https://charleshsliao.wordpress.com/tag/tensorflow/ We will talk about how to apply Recurrent neural network in TensorFlow on both of python and R. RNN might not be the best algorithm to deal with MNIST but this can be… Continue reading RNN in TensorFlow in Python&R, with MNIST

Train Deep Learning Model with R Studio in AWS EC2

AWS provides us with approachable GPU based cloud computing capability with minimal cost. We will talk about the steps to take advantage of AWS EC2 to build GPU computing for our model training in R. 1.  Register AWS account... 2. Find EC2 service 3. Click "launch instance" and go for this one labeled Free tier… Continue reading Train Deep Learning Model with R Studio in AWS EC2

CNN/DNN of KeRas in R, Backend Tensorflow, for MNIST

Keras is a library of tensorflow, and they are both developed under python. We can approach to both of the libraries in R after we install the according packages. Of course, we need to install tensorflow and keras at first with terminal (I am using a MAC), and they can function best with python 2.7.… Continue reading CNN/DNN of KeRas in R, Backend Tensorflow, for MNIST

Identify Arguments of H2O Deep Learning Model with Tuned Auto Encoder in R with MNIST

Auto-encode can be trained to learn the deep or hidden features of data. These hidden features may be used on their own, such as to better understand the structure of data, or for other applications. Two common applications of auto-encoders and unsupervised learning are to identify anomalous data (for example, outlier detection, financial fraud) and… Continue reading Identify Arguments of H2O Deep Learning Model with Tuned Auto Encoder in R with MNIST

Auto encoder with R, MNIST in Deep Learning

Auto-encoders are trained to reproduce or predict the inputs--the hidden layers and neurons are not maps between an input and some other outcome, but are self (auto)-encoding. We can use auto encoders to conduct dimensions reduction, lift overfitting and so on. We will talk about it in the next article. h2o package of R provides… Continue reading Auto encoder with R, MNIST in Deep Learning

Tune Multi-layer Perceptron (MLP) in R with MNIST

Googled MLP and so many "My Little Ponies" results popped out. LOL. 🙂 Generally speaking, a deep learning model means a neural network model with with more than just one hidden layer. Whether a deep learning model would be successful depends largely on the parameters tuned. Multi-layer Perceptron  or MLP provided by R package "RNNS"… Continue reading Tune Multi-layer Perceptron (MLP) in R with MNIST