Introduction: TensorFlow is a machine learning framework for JavaScript applications. It helps us build models that can be directly used in the browser or in the node.js server. We use this framework for building an application that can find similar requests so that they might be used for prediction. 

Description: The JavaScript application uses data from a CSV that has categorizations of requests and the resolution time. The attributes of each request include a category_id, customer id, a pseudo parameter attribute, and the execution time. The data used in this sample has 1200 records but the attributes are minimum to keep the application simple. 

As with any ML learning example, the data is split into 70% training set and 30% test set. There is no order to the data and the split is taken over a random set.  

The model chosen is a Recurrent Neural Network model. This is used for finding groups via paths in sequences. A Sequence Clustering algorithm is like a clustering algorithm mentioned above but instead of finding groups based on similar attributes, it finds groups based on similar paths in a sequence.  A sequence is a series of events. For example, a series of web clicks by a user is a sequence. It can be also be compared to the IDs of any sortable data maintained in a separate table. Usually, there is support for a sequence column. The sequence data has a nested table that contains a sequence ID which can be any sortable data type. 

This is very useful to find sequences of service requests opened across customers. Generally, a network failure could result in a database connection failure which could lead to an application failure. This sort of sequence determination in a data-driven manner helps find new sequences and target them actively even suggesting the same to the customers who open the request so that they can be better prepared. 

Recurrent Neural Networks (RNNs) are a special kind of neural network that works with sequences rather than symbols that constitute the sequence. In fact, this technique does not need to know what the parts of the sequence represent whether they are words or video frames. It can infer the meaning of those symbols. When raw data is shredded into sequences, the RNN keeps state information per sequence that it infers from that sequence. This state is the essence of the sequence. Using this state, the RNN can simply translate input sequences (text) to output sequences (summary). It can also be used to interpret the input sequence to generate an output sequence (like a chatbot). The RNN encoder-decoder model was proposed by Bahdanau et al in 2014 and it can be used to write any kind of decoder that generates custom output.  

TensorFlow makes it easy to construct this model using an API. It can only present the output after the model is executed. In this case, the model must be run before the weights are available.  The output of each layer can be printed using the summary () method.  

With the model and training/test sets defined, it is now as easy to evaluate the model and run the inference.  The model can also be saved and restored. It is executed faster when there is GPU added to the computing. 

The features are available with the feature_extractor. It is evaluated on the training set using model.compile() and model.fit(). The model can then be called on a test input. Additionally, if a specific layer was to be evaluated, we can call just that layer on the test input. 

When the model is tested, it predicts the resolution time for the given attributes of category_id and parameter attribute 

Conclusion: Tensorflow.js is becoming a standard for implementing machine learning models. Its usage is simple, but the choice of model and the preparation of data takes significantly more time than setting it up, evaluating, and using it. 
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