A New Collection of Thoughtful Learning Apps — Now Available on iOS & Android

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I’m excited to share a set of mobile apps I’ve recently completed and published on both the Google Play Store and the Apple App Store. These apps are designed with a simple goal in mind: to make meaningful, structured content more accessible, whether you’re studying theology or improving your English vocabulary. 📱 Now Available on Both Platforms All apps are live and available for download: Google Play Developer Page: https://play.google.com/store/apps/dev?id=5835943159853189043 Apple App Store Developer Page: https://apps.apple.com/ca/developer/q-z-l-corp/id1888794100 📖 Theology & Confession Study Apps For those interested in Reformed theology and classical Christian teachings, I’ve developed a series of apps that present foundational texts in a clean, focused reading format: The Belgic Confession Canons of Dort Heidelberg Catechism Westminster Shorter Catechism Each app is designed to provide a distraction-free experience, making it easier to read, reflect, and revisit these im...
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Create your own dataset for machine learning, same format as CIFAR-10 dataset, via PIL and numpy

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Refers:
http://www.cs.toronto.edu/~kriz/cifar.html
https://www.tensorflow.org/tutorials/deep_cnn


The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes.

ClixSense Click on the cat job use the image that its size is 128 x 96 px, and
the image is cat or dog, no other type.
I want to use CNN to do machine learning for this job. 
I collected some images now.

 The key python codes to append one image with label to the dataset is as below:

 from PIL import Image
import numpy as np   im = Image.open(filename)
  im = (np.array(im))

  r = im[:,:,0].flatten()
  g = im[:,:,1].flatten()
  b = im[:,:,2].flatten()

  if iscat:
    label = [0]
  else:
    label = [1]

  out  = np.array(list(label) + list(r) + list(g) + list(b), np.uint8)
  out.tofile(dataset)

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