R-Brain Blog
Image Recognition with Keras
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In this tutorial, we will use a neural network model for image recognition. We get our data from Kaggle's dogs-vs-cats competition and use Keras, an easy to use python library for deep learning, to train our model to classify images. We will go over the following topics: How to use a convolutional neural network model already trained on different set of images and change it to make it suitable for our own use, how to work with Keras, create batches of images, train and evaluate the accuracy of the image recognition model, and building blocks of the image classification model including convolutional and fully connected layers.
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Natural Language Processing: A simple Chat Bot
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In this tutorial, we'll learn about memory network, an artificial intelligence technique that has the ability to retain information. We'll build a simple chat bot application which answers questions about a simple story. We'll use Keras, a powerful and easy to use python library for deep learning, to build and train the chat bot.
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Introducing R-Brain: A New Data Science Platform
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R-Brain is a next generation platform for data science built on top of Jupyterlab with Docker. It was recently unveiled at JupyterCon in late August. Don’t let the name fool you. R-Brain currently supports R, Python, Structured Query Language (SQL), and more. It has integrated intellisense, debugging, packaging, and publishing capabilities. This cool new solution also has analytics workspace collaboration and marketplace features for personal, professional and enterprise use cases.
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RIDE - A New Data Science IDE for Python and R
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The data science world is split into two parts: the (i)Python and the R community. Both groups offer a plethora of tools and libraries enriching our work-life as a data scientist. Interestingly, many of the offerings are complementary, such that professional data scientists should know both environments to pick the right tool for the job. In many cases, it even makes sense to use Python and R together in the same project. Sadly, today these two worlds don’t integrate very well, so we need to switch back and forth between different tools and environments.
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Visual debugging with RIDE
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On R-Blogger there was an interesting article that compares debugging support for R in RStudio and Eclipse StatET [1]. I liked that article very much, but it misses the new RIDE environment, which I am going to add to the comparison in this article.
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