Shamit Verma
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Help to choose algorithm for computing difference between 2 texts?
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algorithm shouldn't do deep semantic analyzis, only kind of word counting, word vectorization, substring search For such tasks, test if built-in Elastic Search / SOLR models work well. These products ...

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General question on the approach to optimise numbers
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For this, you can treat output provided by "person in charge" as ground truth. I assume that you have historical records of : Numbers generated by this person Parameters that were fed into the model ...

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using neural networks to predict set of charactertics
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Yes, you can generate multiple features as output of network. In this example, network will have X outputs. 1 output will have most probable gender , another will have most probable age and so on. ...

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Suggestions for labeling data for named entity recognition
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While labeling, label with sub-categories. This enables you to work with larger set of tools / algos. With Algos that work better with linear labels (and don't know about label hierarchies), you can ...

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does tensorflow use opencv to covert image to numpy array
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TensorFlow has two ways of processing image data Built-in Image Ops ( https://www.tensorflow.org/api_guides/python/image ) tf.keras Pre-processing ( https://keras.io/preprocessing/image/ ) Feature ...

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Production: TensorFlow and Keras
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Once model has been trained with Keras It can be exported to TensorFlow model OR With tf.keras it can be served as http service Examples : https://towardsdatascience.com/deploying-keras-models-...

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Calculation of Output in LSTM Many-to-One Architecture
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While training, a set of training examples will be provided in a batch. At end of each batch, weights for all layers are updated (Dense and LSTM). https://adventuresinmachinelearning.com/keras-lstm-...

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predict future value in every one hour using (t+60 minutes) LSTM neural network in python
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One option is to : Break data into constant-frequency observations (E.g.: Assume that g,p,c,out are same for all time periods between two observations). With this, you will get samples every N ...

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NLP: What are some popular packages for phrase tokenization?
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Spacy can do this. Spacy's semantic parser is based on Language models trained on large corpus of text. This parser can break sentence into lower level components such as words / phrases. More ...

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Lightweight execution of Spark MLLib models
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You can export the model (Weights + Hyper parameters) to a common format from Spark and then execute the same in another Language/Framework. https://docs.databricks.com/spark/latest/mllib/model-...

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Continous bag of words claimed to be unsupervised, how is it working?
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It is considered "unsupervised" since labels do not have to be created manually. Labels are generated automatically based on how words are used in real world.

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Sentiment analysis for multiple entry in one text
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A Ensemble of "Target based sentiment" models worked for a similar use-case that I worked on. Some of the models that were part of the solution : http://sentic.net/sentic-lstm.pdf https://hal....

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How to make a dataframe with lists or vectors as its elements
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Dataframe is a representation of 2 D matrix in R. R does not support MultiIndex dataframes that can represent more complex structures. Maybe you can model the problem as a Tensor ( https://cran.r-...

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Estimating box size from the contents
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This is known as "3D Bin Packing Problem" in literature. https://en.wikipedia.org/wiki/Bin_packing_problem Since this is NP-Hard; Some of the approaches are : Heuristics : https://www.researchgate....

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