fast.ai - Learning Deep Learning
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Transcript of fast.ai - Learning Deep Learning
Learning Deep Learningfast.ai course
Teemu Kurppa - www.teemukurppa.net Head of Cloud , ŌURA - ouraring.com Twitter - @teemu
www.ouraring.com
the world's first wellness ring
Head of Cloud
I work at
Hype is strongwith this one
A
AI Winters60s70s80s90s
This time it’s different?
Jeremy HowardEntrepreneur & Data Scientist
FastMailfamous email service
Kaggledata science competitions
Enlitic using deep learning for medical imaging
fast.aimaking deep learning accessible to all
fast.ai course - the method
Learn to apply first - learn theory after
7 lessons: - video lectures, 1-2h/each - assignments & reading, +10h/lesson - Kaggle competitions
fast.ai course - Part 1 contentImage Recognition Collaborative Filtering Natural Language Processing
Underfitting Overfitting
Semi-supervised learning CNNs, RNNs, Embeddings
Transfer Learning
Transfer Learning
Finetune this
Reuse this
from vgg16 import Vgg16vgg = Vgg16()
batch_size=64
batches = vgg.get_batches(path+'train', batch_size=batch_size) val_batches = vgg.get_batches(path+’valid’, batch_size=batch_size*2)
vgg.finetune(batches) vgg.fit(batches, val_batches, nb_epoch=1)
vgg.test(path+’test’)
Lesson 1: Code
Gives 97% accuracy (State of the Art 3 years ago 80%)
State of the Art Techniques
2011 Xavier Initialisation2013 VGG2014 Pseudo-labeling2014 Dropout 2015 Batch Normalisation2015 ResNet2016 VGG with Batch Norm (by Jeremy)
AI Garage Helsinki
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Teemu Kurppa - www.teemukurppa.net Head of Cloud , ŌURA - ouraring.com Twitter - @teemu