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Best tool for text representation to deep learning

By : Viswanath
Date : October 14 2020, 02:22 PM
This might help you You should use a pre-trained embedding to represent the sentence into a vector or a matrix. There are a lot of sources where you can find pre-trained embeddings that use different dataset (for instance all the Wikipedia) to train their models. These models can have different length, but normally each word is represented with 100 or 300 dimensions.
Pre-trained embeddings Pre-trained embeddings 2
code :

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Graphical representation of deep-learning network?

By : Rahul Shakya
Date : March 29 2020, 07:55 AM
it helps some times Matrix representation
You will not be modelling the neurons as matrices. Instead you only need to represent the weight layers as individual matrices.
code :
n x m //    n: inputs,   m: outputs
n x m //    n: inputs to this layer,   m: outputs from this layer
input_vector: 1 x n matrix,    n: number of inputs
weight_layer: n x m matrix,    n: number of inputs to this layer     m: number of outputs from this layer

input_vector.dot( weight_layer ) # forward calculation

How to get word vector representation when using Deep Learning in NLP

By : Hadi
Date : March 29 2020, 07:55 AM
it fixes the issue Deep Learning and NLP are quite complex subjects, so if you really want to understand them you'll need to follow a couple of courses in the field and read many papers. There are lots of different techniques for converting words into vector representations and it's a very active area of research. Socher's DL for NLP tutorial is a good next step if you are already well acquainted with NLP and Machine Learning (including deep learning).
With that said (and considering it's a programming forum), if you are just interested for now in using someone's else tools to quickly obtain vector representations which can be useful in some tasks, one library which you must look at is word2vec. Take a look in its website: https://code.google.com/p/word2vec/. It's a very powerful tool and for some basic stuff it could be used without much knowledge.

Which deep learning library support the compression of the deep learning models to be used on the phones?

By : Faisal Gleesh
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further There is a Caffe fork called Ristretto. It allows compressing neural nets for lower numerical precision (less than 32 bits per parameter), while keeping high accuracy. MXNet and Tensorflow also have this feature now. Pytorch doesn't have it yet. These tools allow to reduce the memory required for storing the neural net parameters, but they are not specific to Android.

I want to implement a machine learning or deep learning model for text classification (100 classes)

By : user3425165
Date : March 29 2020, 07:55 AM
seems to work fine An important step in machine learning engineering consists of properly inspecting the data. Herby you get some insight that determines what algorithm to choose. Sometimes, you might try out more than one algorithm and compare the models, in order to be sure, that you tried your best on the data.
Since you did not disclose your data, I can only give you the following advice: If your data is "easy", meaning that you need only little features and a slight combination of them to solve the task, use Naive Bayes or k-nearest neighbors. If your data is "medium" hard, then use Random Forest or SVM. If solving the task requires a very complicated decision boundary combining many dimensions of the features in a non-linear fashion, choose a Neural Network architecture.

Text summarization using deep learning techniques

By : Raghvendra
Date : March 29 2020, 07:55 AM
I wish this help you I think you need to be a little more specific. When you say "I am unable to figure to how exactly the summary is generated for each document", do you mean that you don't know how to interpret the learned features, or don't you understand the algorithm? Also, "deep learning techniques" covers a very broad range of models - which one are you actually trying to use?
In the general case, deep learning models do not learn features that are humanly intepretable (albeit, you can of course try to look for correlations between the given inputs and the corresponding activations in the model). So, if that's what you're asking, there really is no good answer. If you're having difficulties understanding the model you're using, I can probably help you :-) Let me know.
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