artificial-intelligence

Let's say I have filenames which are formatted differently. I want to be able to extract certain aspects from said filename like a human would; pattern recognition. Obviously I can bruteforce myself through with regular expressions but that's not what I'm after. Let's say I have these 4 strings: [MAS] Hayate no Gotoku!! 20 [BD 720p] [21D138F8].mkv [Leopard-Raws] Akatsuki no Yona - 05 RAW (MX 1280x720 x264 AAC).mp4 [BLAST] Wolf Girl and Black Prince - 05 [720p] [C1252A5E].mkv [sage]_Mobile_Suit_Gundam_AGE_-_36_[720p][10bit][45C9E0D0].mkv As you can see all these filenames have certain pattern

My neural network is not giving the expected output after training in Python. Is there any error in the code? Is there any way to reduce the mean squared error (MSE)? I tried to train (Run the program) the network repeatedly but it is not learning, instead it is giving the same MSE and output. Here is the Data I used: https://drive.google.com/open?id=1GLm87-5E_6YhUIPZ_CtQLV9F9wcGaTj2 Here is my code: #load and evaluate a saved model from numpy import loadtxt from tensorflow.keras.models import load_model # load model model = load_model('ANNnew.h5') # summarize model. model.summary() #Model

I am currently working on an implementation of a 2 player trick-and-draw card game, similar to 66 or Schnapsen. Basically you need to gather points by winning tricks and while there are cards in the pack, both players draw a card after each round. I am at the point of programming a good AI for the game that does not cheat, but really calculates the best moves by using only the information it has at the given game state. I am stuck deciding which algorithm or logic would be the best to use. I decided against algorithms like Alpha-Beta pruning because there are too much hidden information

The NeuralDataSet objects that I've seen in action haven't been anything but XOR which is just two small data arrays... I haven't been able to figure out anything from the documentation on MLDataSet . It seems like everything must be loaded at once. However, I would like to loop through training data until I reach EOF and then count that as 1 epoch.. However, everything I've seen all the data must be loaded into 1 2D array from the beginning. How can I get around this? I've read this question, and the answers didn't really help me . And besides that, I haven't found a similar question asked on

I have read a fair amount about Haar training and I'm not clear on how many images one should use for the positive and negative sample sets. I see it recommended to use many images, some people recommend thousands. I'm also unclear of whether the number of positive and negative sample images should be the same? Abid Rahman K Here is the best tutorial on Haar training. Have you tried this? http://note.sonots.com/SciSoftware/haartraining.html It says they used 5000 for positive and 3000 for negative. This link says 3000 for positive and 5000 for negative. Anyway, higher number of images improves