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Previously, we presented you with a simple and effective guide to integrating a Dialogflow bot in a website. In this article, we will be sharing steps to do the same in an Android app. All you need to build a sample chatbot for an android app is Dialogflow and Kommunicate.
Below is an example of Kommunicate Support Bot developed in android using Dialogflow. If you wish to see the bot live in action, head here and click on the chat icon from the bottom right corner.
In our last TensorFlow tutorial, we studied Embeddings in TensorFlow. Today, we will study how to increase our computational workspace by making room for Tensorflow GPU. Moreover, we will see device placement logging and manual device placement in TensorFlow GPU and will discuss optimizing GPU memory. We will also cover single GPU in multiple GPU systems and use multiple GPU in TensorFlow.
Let's begin!
Collaborative filtering (CF)[1] based on the alternating least squares (ALS) technique[2] is another algorithm used to generate recommendations. It produces automatic predictions (filtering) about the interests of a user by collecting preferences from many other users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than a randomly chosen person. This algorithm gained a lot of traction in the data science community after it was used by the team winner of the Netflix Prize.
The CF algorithm has also been implemented in Spark MLlib[3] with the aim to address fast execution on very large datasets. KNIME Analytics Platform with its Big Data Extensions offers it in the Spark Collaborative Filtering node. We will use it here to recommend movies to a new user within a KNIME implementation of the collaborative filtering solution provided in the Infofarm blog post[4].