Machine Learning Part 2 - Neural networks

1. Machine Learning
Part 2. Neural networks

2. Neural networks?

3. Neuron

4. Perceptron

5. Multi-layered
(deep)

6. Problems with
images

7. Problems with
images
Too big vectors
(200x200x3 =
120,000)
Pixel position
matters

8. Convolution

9. Pooling (sub-
sampling)

10. CNN

11. Neural Networks in
Python

12. pip install keras
open/create ~/.keras/keras.json edit
"image_dim_ordering": “th”;
“backend”: “theano”;
open script from: http://bit.ly/
empatika-keras-1
MNIST

13. Dogs & Cats
cd <folder that contains ‘data’
subfolder from Google Drive>
open/create ~/.keras/keras.json edit
“backend”: “tensorflow”;
open script from: http://bit.ly/
empatika-keras-2 (initial gist)

14. Pre-trained models
ResNet50
VGG16
cd <to deep-learning-models
subfolder>

15. ResNet50
from resnet50 import ResNet50
from keras.preprocessing import image
from imagenet_utils import preprocess_input, decode_predictions
import numpy as np
model = ResNet50(weights='imagenet')
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
print('Predicted:', decode_predictions(preds))