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Recognition of elements in a list - machine learning


Recognition of elements in a list - machine learning

By : krellex sumeet
Date : November 21 2020, 04:03 PM
To fix this issue So I have multiple lists: , Maybe something like this could be a starting point:
code :
import numpy as np

# I'll use numbers instead of words,
# but same exact concept
points_list = [[0,1,2],
               [0,3],
               [1,4],
               [0,2]]

scores = np.zeros((5,5))

for points in points_list:
    temp = np.array(points)[:, np.newaxis]       
    scores[temp, points] += 1
>>> scores
array([[ 3.,  1.,  2.,  1.,  0.],
       [ 1.,  2.,  1.,  0.,  1.],
       [ 2.,  1.,  2.,  0.,  0.],
       [ 1.,  0.,  0.,  1.,  0.],
       [ 0.,  1.,  0.,  0.,  1.]])
import numpy as np

# I'll use numbers instead of words,
# but same exact concept
points_list = [[0,1,2],
               [0,3],
               [1,4],
               [0,2],
               [0,1,2,3],
               [0,1,2,4]]

scores = np.zeros((5,5))

for points in points_list:
    temp = np.array(points)[:, np.newaxis]       
    scores[temp, points] += 1


diag = scores.diagonal()

key_col = (scores/diag)[:, 0]
key_col[0] = 0

points_2 = np.where(key_col > 0.5)[0]      # suppose 0.5 is the threshold 
temp_2 = np.array(points_2)[:, np.newaxis] # step 1: we identified the points that are
                                           # close to 0
inner_scores = scores[temp_2, points_2]    # step 1: we are checking if those points are
                                           # are close to each other
>>> scores
array([[ 5.,  3.,  4.,  2.,  1.], # We identified that 1 and 2 are close to 0
       [ 3.,  4.,  3.,  1.,  2.],
       [ 4.,  3.,  4.,  1.,  1.],
       [ 2.,  1.,  1.,  2.,  0.],
       [ 1.,  2.,  1.,  0.,  2.]])
>>> inner_scores
array([[ 4.,  3.],                # Testing to see whether 1 and 2 are close together
       [ 3.,  4.]])               # Since they are, we can conclude that (0,1,2) occur 
                                  # together


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Recognition of a sound (a word) with machine learning in python

Recognition of a sound (a word) with machine learning in python


By : Mahmoud Gamal
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further I've written such program in text recognition. I can tell you if you chose to "teach" your program manually you will have a lot of work think about the variation in voice due to accents etc.
You could start looking for a sound analyzer here (Musical Analysis). try to identify the waves of a simple word like "yes" and write an alghorithm that percentages the variation of the soundfile. this way you can put a margin in to safe yourself from false-positives / vice-versa.
specific character recognition with machine learning

specific character recognition with machine learning


By : Scott R
Date : March 29 2020, 07:55 AM
hop of those help? Okay, if your question is "Can I build this ?", Then the answer is yes. You could train an SVM model to predict the characters based on it's properties (There may be other models, check them out!). As for the question "Can I use a file from paint ?", If you are ready to spend a lot of time drawing random images of all characters, then yes, you can. Rather than doing that, check for any publicly available character datasets, similar to MNIST. And then train the model using that rather, than building your own dataset on paint.
Python Machine Learning Digit Recognition

Python Machine Learning Digit Recognition


By : Meena Chouhan
Date : March 29 2020, 07:55 AM
I wish this help you It looks like you have two issues, which, as you suspected, are related to the pre-processing of your data.
The first is that your image is inverted relative to the training data:
code :
 def mnist_bytescale(image):
    # Use float for rescaling
    img_temp = image.astype(np.float32)
    #Re-zero the data
    img_temp -= img_temp.min()
    #Re-scale and invert
    img_temp /= (img_temp.max()-img_temp.min())
    img_temp *= 255
    return 255 - img_temp.astype('uint')
Machine Learning for gesture recognition with Myo Armband

Machine Learning for gesture recognition with Myo Armband


By : Slartibartfast
Date : March 29 2020, 07:55 AM
like below fixes the issue An easy way to get you started would be to create 161 columns (8 columns for each of the 20 time steps + the designated label). You would rearrange the columns like
code :
emg1_t01, emg2_t01, emg3_t01, ...,  emg8_t20, gesture_id
Machine Learning & Image Recognition: How to start?

Machine Learning & Image Recognition: How to start?


By : Najwen
Date : March 29 2020, 07:55 AM
wish of those help TensorFlow can be used, it's pretty "low-level" though. So if you're just starting out you might be better off using Keras with a TensorFlow backend as it's more userfriendly.
Regarding languages you will probably use Python. So if you don't know it already you should get started. In my opinion you can also learn it on-the-fly while practicing as you're already a developer.
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