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
Has the potential to change many medical specialities
DEEP LEARNING CAN DIAGNOSE PATIENTS
Has the potential to change many medical specialities
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
TRANSFORMING COMPUTATIONAL BIOLOGY
Not just medicine, but genomics too
More here: https://github.com/gokceneraslan/awesome-deepbio
EVERYONE IS USING DEEP LEARNING
WHAT IS 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!
WHAT IS A NEURAL NET?
NEURAL NETWORK STRUCTURE
NEURAL NETWORK STRUCTURE
Say we want to build a model to predict the likelihood of a have a heart attack (MI) based on blood pressure (BP) and BMI
NEURAL NET STRUCTURE
A neural net is a modular way to build a classifier
BP
BP
BMI
BMI
Inputs
Output
Probability of MI
WHAT IS AN ARTIFICIAL NEURON?
The neuron is the basic functional unit a neural network
Inputs
Output
BP
BP
BMI
BMI
Probability of MI
WHAT IS AN ARTIFICIAL NEURON?
The neuron is the basic functional unit a neural network
A neuron does two things, and only two things
BP
BP
BMI
BMI
WHAT IS AN ARTIFICIAL NEURON?
The neuron is the basic functional unit a neural network
Weight for
A neuron does two things, and only two things
Weight for
1) Weighted sum of inputs
w_1*BP + w_2*BMI
w1∗BP+w2∗BMI
BP
BP
BMI
BMI
BP
BP
BMI
BMI
WHAT IS AN ARTIFICIAL NEURON?
The neuron is the basic functional unit a neural network
Weight for
A neuron does two things, and only two things
Weight for
1) Weighted sum of inputs
\phi(w_1*BP + w_2*BMI)
ϕ(w1∗BP+w2∗BMI)
2) Nonlinear transformation
BP
BP
BMI
BMI
BP
BP
BMI
BMI
w_1*BP + w_2*BMI
w1∗BP+w2∗BMI
WHAT IS AN ARTIFICIAL NEURON?
\phi()
ϕ()
is known as the activation function
Sigmoid
Hyperbolic Tangent
WHAT IS AN ARTIFICIAL NEURON?
Summary: A neuron produces a single number that is a nonlinear transformation of its input connections
A neuron does two things, and only two things
= a number
BP
BP
BMI
BMI
WHAT IS AN ARTIFICIAL NEURON?
Summary: A neuron produces a single number that is a nonlinear transformation of its input connections
A neuron does two things, and only two things
= a number
BP
BP
BMI
BMI
This simple formula allows for an amazing amount of expressiveness
NEURAL NETWORK STRUCTURE
BP
BP
BMI
BMI
Inputs
Output
Neural nets are organized into layers
Probability of MI
NEURAL NETWORK STRUCTURE
Inputs
Output
Input Layer
Neural nets are organized into layers
BP
BP
BMI
BMI
Probability of MI
NEURAL NETWORK STRUCTURE
Inputs
Output
Neural nets are organized into layers
1st Hidden Layer
Input Layer
BP
BP
BMI
BMI
Probability of MI
NEURAL NETWORK STRUCTURE
Inputs
Output
Neural nets are organized into layers
A single hidden unit
1st Hidden Layer
Input Layer
BP
BP
BMI
BMI
Probability of MI
NEURAL NETWORK STRUCTURE
Inputs
Output
Input Layer
Neural nets are organized into layers
1st Hidden Layer
A single hidden unit
2nd Hidden Layer
BP
BP
BMI
BMI
Probability of MI
NEURAL NETWORK STRUCTURE
Inputs
Output
Input Layer
Neural nets are organized into layers
1st Hidden Layer
A single hidden unit
2nd Hidden Layer
Output Layer
BP
BP
BMI
BMI
Probability of MI
Finding the best values for the weights = learning
HOW NEURAL NETS LEARN
Weight for
Weight for
BP
BP
BMI
BMI
BP
BP
BMI
BMI
NEURAL NET EXERCISE
Go to: https://playground.tensorflow.org
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
+
KERAS EXERCISE
Go to: https://github.com/beamandrew/HSPH_lecture
CONVOLUTIONAL NEURAL NETWORKS
CONVOLUTIONAL NEURAL NETS (CNNs)
Dates back to the late 1980s
- Invented by in 1989 Yann Lecun at Bell Labs - "Lenet"
- Integrated into handwriting recognition systems
in the 90s - Huge flurry of activity after the Alexnet paper
CONVOLUTIONAL NEURAL NETS
Images are just 2D arrays of numbers
Goal is to build f(image) = 1
CONVOLUTIONAL NEURAL NETS
CNNs look at small connected groups of pixels using "filters"
Image credit: http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution
Images have a local correlation structure
Near by pixels are likely to be more similar than pixels that are far away
CNNs exploit this through convolutions of small image patches
CONVOLUTIONAL NEURAL NETS
Example convolution
CONVOLUTIONAL NEURAL NETS
Pooling provides spatial invariance
Image credit: http://cs231n.github.io/convolutional-networks/
CONVOLUTIONAL NEURAL NETS
Convolution + pooling + activation = CNN
Image credit: http://cs231n.github.io/convolutional-networks/
CONVOLUTIONAL NEURAL NETS (CNNs)
CNN formula is relatively simple
Image credit: http://cs231n.github.io/convolutional-networks/
CONVOLUTIONAL NEURAL NETS
Data augmentation mimics the image generative process
Image credit: http://slideplayer.com/slide/8370683/
- Drastically "expands" training set size
- Improves generalization
- Works if it doesn't "break" image -> label relationship
CNNS AREN'T MAGIC
- Based on solid image priors
- Learns a hierarchical set of filters
- Exploit properties of images, e.g. local correlations and invariances
- Mimic generative distribution with augmentation to reduce over fitting
- Results in end-to-end visual recognition system trained with SGD on GPUs: pixels in -> classifications out
CNNs exploit strong prior information about images
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
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
HOW CAN YOU STAY CURRENT?
The field moves fast, staying up to date can be challenging
http://beamandrew.github.io/deeplearning/2017/02/23/deep_learning_101_part1.html
CONCLUSIONS
- Could potentially impact many fields -> understand concepts so you have deep learning "insurance"
- Long history and connections to other models and fields
- Prereqs: Data (lots) + GPUs (more = better)
- Deep learning models are like legos, but you need to know what blocks you have and how they fit together
- Need to have a sense of sensible default parameter values to get started
- "Babysitting" the learning process is a skill
DEEP LEARNING 101 Andrew Beam, PhD Head of Machine Learning/Senior Fellow Flagship Pioneering October 24th, 2018 twitter: @AndrewLBeam
Deep Learning MMSCI HSHP 2018
By beamandrew
Deep Learning MMSCI HSHP 2018
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