16

You don't need a powerful language for programming AI. Most of the developers are using libraries like Keras, Torch, Caffe, Watson, TensorFlow, etc. Those libraries are highly optimized and handle all the though work, they are built with high performance languages, like C. Python is just there to describe the neural network layers, load data, launch the ...


9

C++ is actually one of the most popular languages used in the AI/ML space. Python may be more popular in general, but as others have noted, it's actually quite common to have hybrid systems where the CPU intensive number-crunching is done in C++ and Python is used for higher level functions. Just to illustrate: http://mloss.org/software/language/c__/ http:...


5

It depends how flexible it needs to be: if you have a fully-fledged system ready for production, which is not going to need much adjusting, then C++ (or even C) might be fine. You need to put a lot of time into building the software, but then it should run pretty fast. However, if you're still experimenting with settings and parameters, and maybe need to ...


5

Yes. Here are some of the most prominent ones and their respective state-of-the-art errors: CIFAR-10: ~3.5% error CIFAR-100: ~24% error STL-10: ~26% error SVHN: ~1.7% error ImageNet tasks: the best 2012 classification task solution got 15% top-5 error, better results are currently available You can check an updated list of solutions here. Also, a more ...


4

The problem you are portraying looks like a modified XOR problem. You can't throw away the lines with a label of 1 because a the model won't be able to learn this class.


3

Your main problem is that you need to separate out what is driving the behaviour policy from the Q-table. Q Learning is an off-policy algorithm. The Q-table that it eventually learns is for an optimal policy (also called the target policy). In order to be able to learn that policy, the agent needs to explore. The usual way to do this is to make the agent ...


2

Software development for AI applications can be separated into programming itself and prototyping. C/C++ is a great language to create the application because it runs very fast and can be delivered as libraries for mainstream operating systems. A precompiled C/C++ application is the gold standard if somebody want's to deploy a turnkey appliance. C++ has a ...


2

First of all, you mention that you have categorical data. I don't see how you can define similarity so that you can also define the distance between the predicted value and the ground truth (error). You can do that only if the data are ordinal. If you want to just classify between normal and anomalous points (binary classification), without caring about ...


2

Before jumping to modeling, there are a few tasks a data scientist (or ML/AI practitioner) must do: Ideation (or hypothesizing): Before applying any modeling approach, we need to ask the right questions. We must clearly mention our assumptions and declare how we want to measure the effectiveness of the pipeline. Note that, some tools/algorithms might not ...


2

Is your question about storing, writing, or reading/processing huge data? I'm not an expert in this topic, but I know a couple of possible ways to handle huge datasets: If the data is too big to be fully uploaded to RAM, you can iterate over it in Pandas. You can find a brief explanation in the article Why and How to Use Pandas with Large Data, section 1. ...


2

If you don't have a lot of data, I suggest you using time series models, such as ARMA, ARIMA, etc. If you have enough data, I suggest you use LSTMs. In both cases, I would aggregate data to some periods, say amount of transactions for month, and use them (and data from all previous months) to predict transactions in the next month.


2

The extraction of features from data and categorizing media are not business decision making. However, determining what patterns in real world information are distinctive and placing an item in one bin rather than another are of a simple kind of decision. That large amounts of data are required to perform these tasks does not discount these limited decision ...


1

Thinking about this more, the answer is in fact yes, but not for the application you mention. You cannot use alpha-beta pruning to learn a model to predict customer outcomes, because it is only useful for domains where you are concerned about an adversary. In finding a customer model, there is no reason to worry about someone coming in and forcing you to ...


1

Yes, you can use sklearn's confusion_matrix. To explicitly extract the false positives and negatives, you can do from sklearn.metrics import confusion_matrix y_true = [0, 1, 0, 1] y_pred = [1, 1, 1, 0] tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()


1

This is perfectly acceptable in a stochastic environment. Generally your loss is to minimize $-log\ p(Y|X)$ or equivalently $-\sum_i log\ p(y_i|x_i)$. This optimization is equivalent to $-\mathbb{E}\log\ p(y_i|x_i)$. In other words you are minimizing in this case: $$ \begin{align*} L &= -log\ p(1|x_0) - log\ p(0|x_0) \\ &= -log [p(1|x_0) * p(0|...


1

You can use the function inverse_transform of the created MinMaxScaler object. See also this Stack Overflow question for other answers and examples.


1

This should be possible, but it's not completely clear what you are trying to do. If you're trying to predict customer age and gender from a video, then you've got a computer vision problem. Deep learning methods are the state of the art for this, and probably some sort of convolutional deep neural network is your friend. If you're trying to predict ...


1

However, doesn't a new training picture destroy the trained and balanced weights and nodes values? Because the weights and hidden nodes have been calibrated to recognize the former training picture? This can happen, and happen to various degrees depending on how the neural network is set up, but it is usually something you want to avoid. Provided a neural ...


1

This is a tricky issue. I assume you are using transition probabilities to pick the next suitable word, so you could use some other corpus data, derive probabilities from it, and compare those to your system. Not very satisfactory, though, as you might end up evaluating the system in a circular way, deriving your test data in the same way as you generate ...


1

Without any additional information, lean towards the vote of the best performing classifier when it comes to ties. However, as others have stated already, it is best to analyze the performances in more detail (e.g. confusion matrices). For instance, it could be that model B almost always classifies class X correctly (hardly any false positives). In that ...


1

A possible application of Decision making systems are interactive computing, knowledge based tutorials and game-based learning. For example, it is possible to model the workflow in a hospital. The expert system has to support the decisions of the human operator. Before the software can explain “good decision”, the domain specific knowledge of the hospital ...


1

Welcome to AI.SE @Par! What you have might be either a multi-label or a multi-class classification problem. If the classes are disjoint (each example belongs to just 1 of the 50 classes), it's a multi-class problem. If not (so each example can belong to several classes at once), it's a multilabel problem. Multi-label classification is usually handled by ...


1

No, there is no difference. Of course, you are likely not able to extrapolate results obtained from synthetic data to expect identical or similar results in real life to unless you have very compelling reasons to do so. Without a more specific question, I'm afraid a more specific answer is not possible.


1

Affluence could encompass several parameters: Income; Wealth (property ownership); Life expectancy; Access to services such as education and health; Access to clean natural resources; Low levels of criminality. Property prices in each locality might be easy to obtain from real estate agent sources Ratings for schools or medical facilities in each area might ...


1

You need to define "simulation" more specific. Playing Mario, Swapping face on image/video, or generating simulation of objects that are orbiting use different techniques. Playing Mario or "AI that playing game": the AI agent trained on available environment (Mario game, so the environment is not generated) and learn the best sequential actions to achieve ...


1

The Project It appears from the question that emotional detection and response is the longer term goal of the project and that recognizing potential emotional manifestations in easily detectable physiological metrics is an initial R&D objective. Mobile device applications are already available to do this, but biometric monitoring via a wristband, ...


1

In general, algorithms that exploit distances or similarities (e.g. in the form of scalar product) between data samples, such as k-NN and SVM, are sensitive to feature transformations. We do feature scaling to make our model robust to outliers and make an initial impact of every feature on the model will be roughly similar Graphical-model based classifiers, ...


1

The term you need is “model ensembles”, that’s the way models are combined. Pretty hard to be more specific since you don’t give a language or any other details.


1

I think that very good exemple in Python is http://amunategui.github.io/reinforcement-learning/


1

Analyzing the existing text with language taggers or with Natural Language Toolkits (NLTK) doesn't make sense. Such software is able to search in the text for words and symbols but it will not understand the meaning. A corpus can not analyzed by itself, text is the output of an underlying model. At first, the domain has to be converted into a conversational ...


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