DEEP LEARNING 101
Andrew Beam, PhD
Head of Machine Learning/Senior Fellow
Flagship Pioneering
October 24th, 2018
twitter: @AndrewLBeam
WHY DEEP LEARNING?
DEEP LEARNING HAS MASTERED GO
DEEP LEARNING HAS MASTERED GO
Source: https://deepmind.com/blog/alphago-zero-learning-scratch/
Human data no longer needed
DEEP LEARNING HAS MASTERED GO
DEEP LEARNING HAS MASTERED GO
DEEP LEARNING CAN PLAY VIDEO GAMES
DEEP LEARNING CAN DIAGNOSE PATIENTS
What does this patient have?
A six-year old boy has a high fever that has lasted for three days. He has extremely red eyes and a rash on the main part of his body in addition to a swollen and red strawberry tongue. Remaining symptoms include swollen lymph nodes in the neck and Irritability
DEEP LEARNING CAN DIAGNOSE PATIENTS
What does this patient have?
A six-year old boy has a high fever that has lasted for three days. He has extremely red eyes and a rash on the main part of his body in addition to a swollen and red strawberry tongue. Remaining symptoms include swollen lymph nodes in the neck and Irritability
Image credit: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
DEEP LEARNING CAN DIAGNOSE PATIENTS
What does this patient have?
A six-year old boy has a high fever that has lasted for three days. He has extremely red eyes and a rash on the main part of his body in addition to a swollen and red strawberry tongue. Remaining symptoms include swollen lymph nodes in the neck and irritability
EVERYONE IS USING DEEP LEARNING
WHAT IS DEEP LEARNING?
Deep learning is a specific kind of machine learning
- Machine learning automatically learns relationships using data
- Deep learning refers to large neural networks
- These neural networks have millions of parameters and hundreds of layers (e.g. they are structurally deep)
- Most important: Deep learning is not magic!
HOW DID WE GET HERE?
NEURAL NETWORKS ARE AN OLD IDEA
One of the very first ideas in machine learning and artificial intelligence
- Date back to 1940s
- Many cycles of boom and bust
- Repeated promises of "true AI" that were unfulfilled followed by "AI winters"
Are today's neural nets any different than their predecessors?
"[The perceptron is] the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence." - Frank Rosenblatt, 1958
IN THE BEGINNING... (1940s-1960s)
Rosenblatt's Perceptron, 1957
- Initially very promising
- Came with provably correct learning algorithm
- Could recognize letters and numbers
THE FIRST AI WINTER (1969)
Minsky and Papert show that the perceptron can't even solve the XOR problem
Kills research on neural nets for the next 15-20 years
THE BACKPROPAGANDISTS EMERGE (1986)
Rumelhart, Hinton, and Willams show us how to train multilayered neural networks
REBRANDING AS 'DEEP LEARNING' (2006)
Unsupervised pre-training of "deep belief nets" allowed for large and deeper models
Image credit: https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks
THE BREAKTHROUGH (2012)
Imagenet Database
- Millions of labeled images
- Objects in images fall into 1 of a possible 1,000 categories
- Relatively high-resolution
- Bounding boxes giving exact location of object - useful for both classification and localization
Large Scale Visual
Recognition Challenge (ILSVRC)
- Annual Imagenet Challenge starting in 2010
- Successor to smaller PASCAL VOC challenge
- Many tracks including classification and localization
- Standardized training and test set. Competitors upload predictions for test set and are automatically scored
THE BREAKTHROUGH (2012)
Pivotal event occurred in the 2012 ILSVRC which brought together 3 critical ingredients:
- Massive amounts of labeled images
- Training with GPUs
- Methodological innovations that enabled training deeper networks while minimizing overfitting
THE BREAKTHROUGH (2012)
In 2011, a misclassification rate of 25% was near state of the art on ILSVRC
In 2012, Geoff Hinton and two graduate students, Alex Krizhevsky and Ilya Sutskever, entered ILSVRC with one of the first deep neural networks trained on GPUs, now known as "Alexnet"
Result: An error rate of 16%, nearly half what the second place entry was able to achieve.
The computer vision world immediately took notice
THE ILSVRC AFTERMATH (2012-2014)
Alexnet paper has ~ 30,000 citations since being published in 2012!
WHY NOW?
MODERN DEEP LEARNING
Several key advancements have enabled the modern deep learning revolution
Advent of massively parallel computing by GPUs.
MODERN DEEP LEARNING
Several key advancements have enabled the modern deep learning revolution
Advent of massively parallel computing by GPUs.
21 TFLOPs of computing power. Would have been the fastest super computer on Earth around the year 2000!
You can rent one on the Amazon cloud for $3/hour!
Several key advancements have enabled the modern deep learning revolution
Methodological advancements have made deeper networks easier to train
Architecture
Optimizers
Activation Functions
MODERN DEEP LEARNING
Several key advancements have enabled the modern deep learning revolution
Robust frameworks and abstractions make iteration faster and less error prone
Automatic differentiation allows easy prototyping
MODERN DEEP LEARNING
+
CASE STUDY
DIABETIC RETINOPATHY DETECTION
DIABETIC RETINOPATHY DETECTION
DIABETIC RETINOPATHY DETECTION
DIABETIC RETINOPATHY
Why did this work so well?
- Huge dataset of over 100,000 images
- High quality annotations - each image was rated by 3-7 opthamologists
- Transfer learning - neural network was originally trained on Imagenet!
- For the cost of a GPU (~$1,000) it's possible to read 240 million images/day at accuracy on par with best ophthalmologists!
DIABETIC RETINOPATHY
Implications
- Many subsequent studies have followed this formula
- Ingredients: Deep learning + high quality database of ~100,000 medical images + transfer learning
- Many medical imaging tasks in radiology, pathology, dermatology, and opthamology can be fully automated in a similar manner
- Similar results emerging from non-image data
How will this technology change medical practice, reimbursement, and other policies?
SUMMARY & CONCLUSIONS
DEEP LEARNING AND YOU
Barrier to entry for deep learning is actually low
... but a few things might stand in your way:
- Need to make sure your problem is a good fit
- Lots of labeled data and appropriate signal/noise ratio
- Access to GPUs
- Must "speak the language"
- Many design choices and hyper parameter selections
- Know how to "babysit" the model during learning phase
GCRSC 2018
By beamandrew
