Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala it integrated with Hadoop and Spark, to be used in business environments on distributed GPUs and CPUs that aims to be cutting-edge plug and play, more convention than configuration, which allows for fast prototyping for non-researchers.
It is well documented with a lot of examples, the examples include a complete impelementation of one of the well-known papers in Natrual Language proccessing, the community is active, stilling rolling out newer versions both based on the feedback from the users and to add new features and the authors provide a complete book to explain their work.
What do you dislike?
It still suffers from few bugs, for example, the neural network output function is not synchronized and it took me so long to discover that as no the error was not clear. It might not be a good option if you want to use it in a large scale project as my imperssion is that it is still under development.
Recommendations to others considering the product:
Don't use it in large-scale projects, it still suffers from some bugs.
What problems are you solving with the product? What benefits have you realized?
We are trying to implement a sentiment analyzer that could be used to classify the tweets to positive/negative and then visualize the data to the users to get a general idea about the current trend about products or events.
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