<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Netvouz / henrik / tag / keras</title>
<link>http://netvouz.com/henrik/tag/keras?feed=rss</link>
<description>henrik&#39;s bookmarks tagged &quot;keras&quot; on Netvouz</description>
<item><title>Install Anaconda, Jupyter Notebook, TensorFlow and Keras for Deep Learning</title>
<link>https://medium.com/@margaretmz/anaconda-jupyter-notebook-tensorflow-and-keras-b91f381405f8</link>
<description>So you want to get started to study deep learning? The first step is to set up the tools. In this post I will share with you how to set up Anaconda and Jupyter Notebook, and then install TensorFlow (including Keras).</description>
<category domain="http://netvouz.com/henrik?category=968349102261480250">Artificial Intelligence AI</category>
<author>henrik</author>
<pubDate>Mon, 05 Aug 2019 10:12:01 GMT</pubDate>
</item><item><title>Time Series Prediction Using LSTM Deep Neural Networks</title>
<link>https://www.altumintelligence.com/articles/a/Time-Series-Prediction-Using-LSTM-Deep-Neural-Networks</link>
<description>This article focuses on using a Deep LSTM Neural Network architecture to provide multidimensional time series forecasting using Keras and Tensorflow - specifically on stock market datasets to provide momentum indicators of stock price. The code for this framework can be found in the following GitHub repo (it assumes python version 3.5.x and the requirement versions in the requirements.txt file. Deviating from these versions might cause errors): https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction The following article sections will briefly touch on LSTM neuron cells, give a toy example of predicting a sine wave then walk through the application to a stochastic time series. The article assumes a basic working knowledge of simple deep n</description>
<category domain="http://netvouz.com/henrik?category=968349102261480250">Artificial Intelligence AI</category>
<author>henrik</author>
<pubDate>Tue, 23 Jul 2019 14:25:44 GMT</pubDate>
</item><item><title>Why does Keras LSTM batch size used for prediction have to be the same as fitting batch size? - Stack Overflow</title>
<link>https://stackoverflow.com/questions/43702481/why-does-keras-lstm-batch-size-used-for-prediction-have-to-be-the-same-as-fittin</link>
<description>When using a Keras LSTM to predict on time series data I&#39;ve been getting errors when I&#39;m trying to train the model using a batch size of 50, while then trying to predict on the same model using a batch size of 1 (ie just predicting the next value). Why am I not able to train and fit the model with multiple batches at once, and then use that model to predict for anything other than the same batch size. It doesn&#39;t seem to make sense, but then I could easily be missing something about this.</description>
<category domain="http://netvouz.com/henrik?category=968349102261480250">Artificial Intelligence AI</category>
<author>henrik</author>
<pubDate>Fri, 20 Sep 2019 18:45:34 GMT</pubDate>
</item></channel></rss>