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Trying to address all the questions asked in the end in the same order Most definitely possible. I would say its best you approach this with segmentation to start with. Just use a free GPU runtime notebook service such as Google Colab or Kaggle Kernels. But you would not directly be able to integrate with the device, you'd have to keep moving input and ...


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Hi and welcome to the community. It's important to understand these basic concepts very clearly. You have to first understand the basic unit of a neural network, a single node/neuron/perceptron. Let us forget all about Neural Networks for a bit, and talk about something far simpler. Linear Regression In the above figure, we clearly have one independent ...


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The output of any node is simply a scalar number. For a given input you get a specific scalar output. What is being shown is the surfaces that get generated as you VARY x1 and x2 over their input range. To answer your first question it is always best to scale your inputs.


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While, as you begin to hit on, there are general guidelines to follow when building a neural network, they are far from standardized. This is because even though AI is a reasonably old field(1950s), neural networks have only been the tool of choice for less than a decade. Before, NNs did horribly, due to lack of data, and computation along with some less ...


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Yes a variant of NLP processing could help find the correct number to extract and type of object in this data. Compared to the spreadsheet, the raw text data is ambiguous without understanding language to a reasonable depth, and without knowing the business context in order to extract the relevant information. For instance, you are expecting to extract "...


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It depends on the complexity on your sentences. If you have a limited range, you could do simple pattern matching on part-of-speech tags. Put your sentence through a tagger (there are plenty of them around) and look for the first noun following a verb: I want an apple Pronoun verb determiner noun (I assume you mean the object, as the subject ...


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