@mo.shakirma I had to install these to get the notebooks running
pip install xgboostpip install gensimpip install keras-tqdm
@mo.shakirma pip install xgboost
I shared a link on the part 1 forum. anyway here it is: https://github.com/dunovank/jupyter-themes
for me it was:
pip install matplotlibpip install pandaspip install xgboostpip install bcolzpip install gensimpip install nltkpip install keras_tqdm
from utils2 import * caused all that dependencies
from utils2 import *
I found out about Part I of this course from Import AI.
If you import the VGG model that is built into keras (keras.applications), do you still have to re-order the channels, etc.?
I like the Import AI and Wild ML newsletters: http://www.wildml.com/newsletter/
Shouldn't we use something like Resnet instead of VGG (with avg pooling) since the residual blocks carry more context?
Should/do we put in batch normalization like we did in lesson 5?
Will the pre-trained weights change if we're using Average pooling instead of max pooling?
how about vgg16_bn_avg?
MaxPool --> AvgPool change still preserves the "pretrained"?
Do you have to retrain VGG on imagenet when you change max pooling to avg pooling?
Is it advisable/recommended to learn Tensorflow?
Can jeremy scroll down a bit on the screen when he goes over code snippets so they are centered on the board?
Can you walk us through [loss]+grads ? Why is there a plus sign there and what does it do?
It's python syntax to concatenate lists. (Make loss into a list, then concatenate it w/ grads.)
So it's just [loss, grads] flattened?