# Tag Info

10

Great question! NN is very promising for this type of problem: Giraffe Chess. Lai's accomplishment was considered to be a pretty big deal, but unfortunately came just a few months before AlphaGo took the spotlight. (It all turned out well, in that Lai was subsequently hired by DeepMind, although not so well for the Giraffe engine;) I've found Lai's ...

8

You shouldn't assign all to 0.5 because you'd have the "break symmetry" issue. http://www.deeplearningbook.org/contents/optimization.html Perhaps the only property known with complete certainty is that the initial parameters need to “break symmetry” between different units. If two hidden units with the same activation function are connected to ...

7

I'm a chess player and my answer will be only on chess. Training a neutral network with reinforcement learning isn't new, it has been done many times in the literature. I'll briefly explain the common strategies. The purpose of a network is to learn position evaluation. We all know a queen is stronger than a bishop, but can we make the network know about ...

6

Initial state How things are at first. In your particular example, it would be where your k knights are placed on the board initially. Your problem doesn't precisely state this, so you could either place them at the bottom or at random. Goal state The board with the k knights placed on the target squares. State transition function A function that ...

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Correlation Between Entries The first recommendation is to ensure that appropriate warning and informational entries in the log file are presented along with errors into the machine learning components of the solution. All log entries are potentially useful input data if it is possible that there are correlations between informational messages, warnings, ...

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If it was based on how the human brain learns, it might have used hebbian learning. One example for such a network would be HTM.

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The initial weights in a neural network are initialized randomly because the gradient based methods commonly used to train neural networks do not work well when all of the weights are initialized to the same value. While not all of the methods to train neural networks are gradient based, most of them are, and it has been shown in several cases that ...

6

This answer applies to Machine Learning (ML) part of AI, as that seems to be what you are asking about. Please bear in mind that AI is still a broad church, including many other techniques than ML. ML, including neural networks for deep learning, and Reinforcement Learning (RL) is only a subset of AI - some AI techniques are more focused on the algorithm ...

5

Your scenario is common. The most straightforward approach is to subsample your data randomly. Unless your data or your model has strong bias, your performance to the smaller data set should be comparable. The accuracy might be lower, but the purpose is to do quick sanity check.

5

There are several approaches as to how this can be achieved. One recent study from 2015 about Action Recognition in Realistic Sports VideosPDF uses the action recognition framework based on the three main steps of feature extraction (shape, post or contextual information), dictionary learning to represent a video, and classification (BoW framework). A few ...

5

This study from 2012 uses 3D convolutional neural networks (CNN) for automated recognition of human actions in surveillance videos. The 3D CNN model extracts features from both the spatial and the temporal dimensions by performing 3D convolutions, thereby capturing the motion information encoded in multiple adjacent frames. A very similar deep learning ...

5

Neuroevolution Through Augmenting Topologies or NEAT may be what you are referring to. The original paper by Kenneth O. Stanley is here NEAT combines a neural network and a genetic algorithm. Instead of using back propagation or gradient descent to "train" your network, NEAT creates a population of very simple neural networks (no connections) and evolves ...

4

If the system claims that a piece of code has violated standards, then to be useful to the programmer, it really needs to provide more information than just a 'yes/no' classifier: you need some form of explanation about why it is claimed to be wrong. Clearly ANNs aren't much use for that. If I were tackling such a problem (and my suspicion is that a lot of ...

4

For a finite value to be 'optimal,' typically you need some benefit from more paired up with some cost for more, and eventually the lines cross because the benefit decreases and the cost increases. Most models will have a reduction in error with more training data, that asymptotically approaches the best the model can do. See this image (from here) as an ...

4

There are approaches to training neural networks that do not use back-propagation, or genetic algorithms. One example is the Extreme Learning Machine approach. You may find something useful in this older discussion on Cross Validated.

4

I think you should get familiar with reinforcement learning. In this field of machine learning the agent interacts whit its environment and after that the agent gets some reward. Now, the agent is the neural network the environment is the game and the agent can get a reward +1 if it wins or -1 if loses. You can use this state, action, reward experienc tuple ...

4

At first, you can find lots of information as pedestrian detection. As you are trying to localize game characters, the face is not the best option. You need to look for the character in general. About HAAR Cascades, the algorithm is one of the fastest face localization solutions in the market. The reason is, it applies all the feature classifications layer ...

4

This derivative is used when calculating the error of your machine learning algorithm during gradient based minimization methods. Read below for more info. When performing supervised classification (with X, Y data vectors of inputs and outcome data to train with) you begin with the error function E(X, Y; θ)= ∑i (ƒ(xi; θ)-yi)2 for ...

4

The usual parameters to adjust in a k-means: Number of clusters (recall many clusters can have same label). Distance definition (euclidean is the most basic, Gauss is an improvement) Selection of initial cluster positions. Data preprocessing (data normalization, ...)

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Randomising just b sort of works, but setting w to all zero causes severe problems with vanishing gradients, especially at the start of learning. Using backpropagation, the gradient at the outputs of a layer L involves a sum multiplying the gradient of the inputs to layer L+1 by the weights (and not the biases) between the layers. This will be zero if the ...

4

In many cases, a production-ready model has everything it needs to make predictions without retaining training data. For example: a linear model might only need the coefficients, a decision tree just needs rules/splits, and a neural network needs architecture and weights. The training data isn't required as all the information needed to make a prediction is ...

4

I believe this can best be done with reinforcement learning via Deep Q Learning. That's where I would start. Steps are: Initialize a Q table. Choose an action. Perform the action. Measure the reward. Update the Q. A neural net will approximate the Q function. See: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-...

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I'm going to assume here that you're using the standard, basic, simple variant of $Q$-learning that can be described as tabular $Q$-learning, where all of your state-action pairs for which you're learning $Q(s, a)$ values are represented in a tabular fashion. For example, if you have 4 actions, your $Q(s, a)$ values are likely represented by 4 matrices (...

4

In short I mentioned in another post, how the Artificial Neural Network (ANN) weights are a relatively crude abstraction of connections between neurons in the brain. Similarly, the random weight initialization step in ANNs is a simple procedure that abstracts the complexity of central nervous system development and synaptogenesis. A bit more detail (with ...

4

What are the trained models? are they algorithms or a collection of parameters in a file? "Model" could refer to the algorithm with or without a set of trained parameters. If you specify "trained model", the focus is on the parameters, but the algorithm is implicitly part of that, since without the algorithm, the parameters are just an arbitrary set of ...

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.

4

It actually depends on a couple of things here - How many output classes do you have? If you have only 2 or 3 classes, it is a very easy task for the classifier that you have built. So, it is highly possible that convergence has occurred. As @Djib2011 mentioned already, if your input training set is not balanced and is heavier with one of the output classes ...

3

You may play around on an average laptop but training will be very slow and you will be limited on the size of your model. Once you try to build something more serious you will run out of memory very fast. A system with a GPU is recommended if you want to really do things like image recognition. If you buy something I would not go for any GPU with less than ...

3

Your question depends heavily on the method you are using for machine learning. It sounds like you want to extract certain features like "curves and straight lines" from your images and use them as training data. This step of extraction is usually not considered part of the training process but part of pre-processing. During pre-precessing you read your ...

3

In recent times different data science magazines and institutions have published their reviews of the top AI toolkits. In these reviews they tend to highlight the innovative features possessed by each platform as well as their reliability and ability to scale. Below are a some evaluations of AI platforms that I recommend you have a look at: KDnuggets ...

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