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Heuristic, black-box optimization algorithms (such as particle swarm optimization, differential evolution, etc.) are popular and powerful alternatives to gradient-based methods. Are there any population-based methods for multi dimensional, nonlinear root-finding, beyond the trivial approach of using a population-based optimization algorithm to minimize the SSE of the residuals.
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I've heard of the concept of "sending" and "receiving" data via an API. "Consuming" data would be "receiving" the data. In terms of "consuming" data, what would be its corresponding opposite term?
Serving
Think of it as the API as a waiter serving your meal, and you consuming it.
In this article the authors use serving as an opposite to consuming: https://stackoverflow.blog/2020/03/02/best-practices-for-rest-api-design/
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Should I normalize input data to normal distribution before fit it into RNN? If yes, why? At the moment almost all the columns are right shifted, so it's not a normal distribution at all.
You do not necessarily need to transform the inputs to a normal distribution, but you might want to preprocess them so that the majority of each of their values is between 0 and 1. Otherwise, when using sigmoid functions for internal nodes, you may cause saturation. If your inputs are each U(0, 1000), for example, then there's no need to transform to normal distributions, but rather to just scale by 0.001.
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I have read blogpost about TensorFlow is being open sourced.
In the tutorials and the examples on the TensorFlow website, I see that they are mostly classification problems. (e.g. given an image, classify the number written in it)
I am curious about it the software also suitable for solving problems in recommendation system?
For example, is it good for solving problems on collaborative filtering / content-based filtering?
Tensorflow is great for deep learning, or training large neural nets. Although, it can be used for several other mathematical applications such as PDEs, various classifiers, recommendation systems etc, there doesn't seem to have a lot of support for them as yet.
This reddit thread might be a good place to start for searching libraries which are centred around recommendation systems.
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I have realized that most of the problems that I solve on a day to day basis are done via two programming techniques: iteration or recursion.
Are there other techniques out there? Any book recommendations or online references?
the programming techniques that you use to solve the problems can be divided into types of algorithms (not into the loop or technique they use in there program, like you mentioned). some of the methods are..
1. Divide and conquer
2. greedy
3. dynamic programming
you can refer this link to read more..
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What is the measurement of the application's reliability as a non-functional requirement?
You cannot measure reliability as it is linked to a bunch of different factors. Uptime can be one of them, for web apps. For other software, it can be something like the prevention of data loss under any circumstances.
You'll have to define it more exactly by breaking it down into your detailed expectations, then you can quantify values to measure against.
It is a core problem with nonfunctional requirements that you cannot measure them easily. You'll have to work around that as described above.