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DEEP LEARNING 101

Andrew Beam, PhD

Postdoctoral Fellow

Department of Biomedical Informatics

Harvard Medical School

February 24th, 2017

twitter: @AndrewLBeam

GOAL OF THIS TALK

THIS TALK IS A MULTIMEDIA EXPERIENCE

Deep Learning 101 companion series of blog posts:

http://beamandrew.github.io

Jupyter Notebooks:

https://github.com/beamandrew/deeplearning_101

DEEP LEARNING HAS MASTERED GO

DEEP LEARNING CAN PLAY VIDEO GAMES

DEEP LEARNING CAN IMITATE STYLE

EVERYONE IS USING DEEP LEARNING

WHAT IS A NEURAL NET?

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)

  Warren McCulloch and Walter Pitts (1943)

​​Thresholded logic unit with hardcoded weights

Intended to mimic "integrate and fire" model of neurons

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

Pivotal event occurred in an image recognition contest which brought together 3 critical ingredients for the first time:

• Massive amounts of labeled images
• Training with GPUs
• Methodological innovations that enabled training deeper networks while minimizing overfitting 

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 ~ 10,000 citations since being published in 2012!

NETS KEEP GETTING DEEPER (2012-2016)

WHAT'S CHANGED? WHY NOW?

Several key advancements has enabled the modern deep learning revolution

Several key advancements have enabled the modern deep learning revolution

Availability of massive datasets

with high-quality labels

Standardized benchmarks of progress and open source tools

Community acknowledgment that

open data -> everyone gets better

Advent of massively parallel computing by GPUs. Enabled training of huge neural nets on extremely large datasets

Methodological advancements have made deeper networks easier to train

Architecture

Activation Functions

Robust frameworks and abstractions make iteration faster and less error prone

Automatic differentiation allows easy prototyping

This all leads to the following hypothesis

Deep Learning Hypothesis:  The success of deep learning is largely a success of engineering.

Personal belief: Things are different with neural nets this time around

Fitting Analogy?

These advancements have been transferred to other fields

Doctors were crucial... in creating the labeled dataset!

Off the shelf, pre-trained deep neural network + 130,000 images = expert level diagnostic accuracy

Not just medicine, but genomics too

More here: https://github.com/gokceneraslan/awesome-deepbio

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

DEEP LEARNING AND YOU

Barrier to entry for deep learning is actually low

... but a few things might stand in your way:

• Lots of labeled data and appropriate signal/noise ratio
• Access to GPUs
• Many design choices and hyper parameter selections
• Know how to "babysit" the model during learning phase

Introduction to Deep Learning Models

Multilayer perceptrons (mlps).

• Most generic form of a neural net is the "multilayer perceptron"
• Input undergoes a series of nonlinear transformation before a final classification layer

• MLPs are the easiest entry point to understand what's going on in a deep learning model
• Closely related to logistic regression

LOGISTIC REGRESSION REFRESHER

Pretend we just have two variables: 

... and a class label:

... we construct a function to predict y from x:

... and turn this into a probability using the logistic function:

... and use Bernoulli negative log-likelihood as loss:

This is good old-fashioned logistic regression

How do we learn  the "best" values for                              ?

Gradient Decscent

• Give weights random initial values
• Evaluate partial derivative of each weight with respect negative log-likelihood at current weight value
• Take a step in direction opposite to the gradient
• Rinse and repeat

This in essence is the entire "learning" algorithm

behind modern deep learning. Keep this in mind.

LOGISTIC REGRESSION -> MLP

With a small change, we can turn our logistic regression model into a neural net

• Instead of just one linear combination, we are going to take several, each with a different set of weights (called a hidden unit)
• Each linear combination will be followed by a nonlinear activation
• Each of these nonlinear features  will be fed into the logistic regression classifier
• All of the weights are learned end-to-end via SGD

MLPs learn a set of nonlinear features directly from data

"Feature learning" is the hallmark of deep learning approachs

Can add more layers to increase capacity of network

MLP IN 8 LINES

See this notebook for more details: https://github.com/beamandrew/deeplearning_101/blob/master/mlp_tutorial.ipynb

Tensorflow backend gives gradients, training procedure, GPU computation "for free"!

CONVOLUTIONAL NEURAL NETS (CNNs)

CNNs are the workhorse model in image recognition

• Simplified version of MLP
• "Local" weight connections
• Responsible for most of the initial deep learning breakthroughs
• Tailored specifically for characteristics  of image recognition

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

CNNs exploit strong prior information about images

If we just "flatten" the image into a vector, we throw away a ton  of information

CNNs use the structural properties of images to improve performance. 

What's a convolution?

In images, it just means that we're doing a dot-product over a small image patch

Example convolution

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

Pooling provides spatial invariance

Image credit: http://cs231n.github.io/convolutional-networks/

Convolution + pooling + activation = CNN

CNN formula is relatively simple

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

CNN 12 LINES OF CODE

Again, tensorflow backend gives gradients, training procedure, GPU computation "for free"!

See this notebook ( soon  to be published) for more details: https://github.com/beamandrew/deeplearning_101/blob/master/cnn_tutorial.ipynb

ADVERSARIAL EXAMPLES

CNNs can be tricked in strange ways

Image credit: https://openai.com/blog/adversarial-example-research/

Works on "real" photos too

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

HOW TO GET STARTED

https://wiki.med.harvard.edu/Orchestra/OrchestraNvidiaGPUs

HMS has GPU resources

tensorflow: module load dev/tensorflow/0.12- GPU

CONCLUSIONS

• Deep learning is real and probably here to stay
• 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

By beamandrew

More from beamandrew

lectures-labs

Slides and jupyter notebooks for the deep learning lectures at master year 2 data science from institut polytechnique de paris, deep learning course: lecture slides and lab notebooks.

This course is being taught at as part of Master Year 2 Data Science IP-Paris

Table of contents

The course covers the basics of Deep Learning, with a focus on applications.

Lecture slides

• Intro to Deep Learning
• Neural Networks and Backpropagation
• Embeddings and Recommender Systems
• Convolutional Neural Networks for Image Classification
• Deep Learning for Object Detection and Image Segmentation
• Recurrent Neural Networks and NLP
• Sequence to sequence, attention and memory
• Expressivity, Optimization and Generalization
• Imbalanced classification and metric learning
• Unsupervised Deep Learning and Generative models

Note: press “P” to display the presenter’s notes that include some comments and additional references.

Lab and Home Assignment Notebooks

The Jupyter notebooks for the labs can be found in the labs folder of the github repository :

These notebooks only work with keras and tensorflow Please follow the installation_instructions.md to get started.

Direct links to the rendered notebooks including solutions (to be updated in rendered mode):

Lab 1: Intro to Deep Learning

• Demo: Object Detection with pretrained RetinaNet with Keras
• Intro to MLP with Keras

Lab 2: Neural Networks and Backpropagation

• Backpropagation in Neural Networks using Numpy
• Bonus: Backpropagation using TensorFlow

Lab 3: Embeddings and Recommender Systems

• Short Intro to Embeddings with Keras
• Neural Recommender Systems with Explicit Feedback
• Neural Recommender Systems with Implicit Feedback and the Triplet Loss

Lab 4: Convolutional Neural Networks for Image Classification

• Convolutions
• Pretrained ConvNets with Keras
• Fine Tuning a pretrained ConvNet with Keras (GPU required)
• Bonus: Convolution and ConvNets with TensorFlow

Lab 5: Deep Learning for Object Dection and Image Segmentation

• Fully Convolutional Neural Networks
• ConvNets for Classification and Localization

Lab 6: Text Classification, Word Embeddings and Language Models

• Text Classification and Word Vectors
• Character Level Language Model (GPU required)
• Transformers (BERT fine-tuning): Joint Intent Classification and Slot Filling

Lab 7: Sequence to Sequence for Machine Translation

• Translation of Numeric Phrases with Seq2Seq

Lab 8: Intro to PyTorch

• Pytorch Introduction to Autograd
• Pytorch classification of Fashion MNIST
• Stochastic Optimization Landscape in Pytorch

Lab 9: Siamese Networks and Triplet loss

• Face verification using Siamese Nets
• Face verification using Triplet loss

Lab 10: Variational Auto Encoder

• VAE on Fashion MNIST

Acknowledgments

This lecture is built and maintained by Olivier Grisel and Charles Ollion

Charles Ollion, head of research at Heuritech - Olivier Grisel, software engineer at Inria

We thank the Orange-Keyrus-Thalès chair for supporting this class.

All the code in this repository is made available under the MIT license unless otherwise noted.

The slides are published under the terms of the CC-By 4.0 license .

Browse Course Material

Course info, instructors.

• Alexander Amini
• Ava Soleimany

Departments

• Electrical Engineering and Computer Science

As Taught In

• Artificial Intelligence
• Graphics and Visualization
• Software Design and Engineering

Learning Resource Types

Introduction to deep learning, course description.

You are leaving MIT OpenCourseWare

Course Summary

This course is an elementary introduction to a machine learning technique called deep learning (also called deep neural nets), as well as its applications to a variety of domains, including image classification, speech recognition, and natural language processing. Along the way the course also provides an intuitive introduction to basic notions such as supervised vs unsupervised learning, linear and logistic regression, continuous optimization (especially variants of gradient descent), generalization theory and overfitting, regularizers, and probabilistic modeling. The homeworks explore key concepts and simple applications, and the final project allows an in-depth exploration of a particular application area.

Administrative Information

Lectures: mon + wed, 3:00-4:20 in friend center, room 109..

Instructor: Yingyu Liang , CS building 103b, Reception hours: Thu 3:00-4:00

Teaching Assistant: Bochao Wang , Electrical Engineering, C319B, Reception hours: Mon + Tue, 11:00-12:00

Requirements: COS 126 General Computer Science (or equivalent) and COS 340 Reasoning About Computation. Mostly need linear algebra, calculus, probability, and some programming knowledge.

Attendance and the use of electronic devices: Attendance is expected at all lectures. The use of laptops and similar devices for note-taking is permitted.

Textbook and readings

Grading and collaboration, lecture notes and schedule.

Deep Learning

An mit press book in preparation, ian goodfellow, yoshua bengio and aaron courville.

We plan to offer lecture slides accompanying all chapters of this book. We currently offer slides for only some chapters. If you are a course instructor and have your own lecture slides that are relevant, feel free to contact us if you would like to have your slides linked or mirrored from this site.

• Presentation of Chapter 1, based on figures from the book [ .key ] [ .pdf ]
• Video of lecture by Ian and discussion of Chapter 1 at a reading group in San Francisco organized by Alena Kruchkova
• Linear Algebra [ .key ][ .pdf ]
• Probability and Information Theory [ .key ][ .pdf ]
• Numerical Computation [ .key ] [ .pdf ] [ youtube ]
• Machine Learning Basics [ .key ] [ .pdf ]
• Video (.flv) of a presentation by Ian and a group discussion at a reading group at Google organized by Chintan Kaur.
• Regularization for Deep Learning [ .pdf ] [ .key ]
• Gradient Descent and Structure of Neural Network Cost Functions [ .key ] [ .pdf ] These slides describe how gradient descent behaves on different kinds of cost function surfaces. Intuition for the structure of the cost function can be built by examining a second-order Taylor series approximation of the cost function. This quadratic function can give rise to issues such as poor conditioning and saddle points. Visualization of neural network cost functions shows how these and some other geometric features of neural network cost functions affect the performance of gradient descent.
• Tutorial on Optimization for Deep Networks [ .key ] [ .pdf ] Ian's presentation at the 2016 Re-Work Deep Learning Summit. Covers Google Brain research on optimization, including visualization of neural network cost functions, Net2Net, and batch normalization.
• Batch Normalization [ .key ] [ .pdf ]
• Video of lecture / discussion : This video covers a presentation by Ian and group discussion on the end of Chapter 8 and entirety of Chapter 9 at a reading group in San Francisco organized by Taro-Shigenori Chiba.
• Convolutional Networks [ .key ][ .pdf ] A presentation summarizing Chapter 9, based directly on the textbook itself.
• Sequence Modeling [ .pdf ] [ .key ] A presentation summarizing Chapter 10, based directly on the textbook itself.
• Video of lecture / discussion. This video covers a presentation by Ian and a group discussion of Chapter 10 at a reading group in San Francisco organized by Alena Kruchkova.
• Practical Methodology [ .key ][ .pdf ] [ youtube ]
• Applications [ .key ][ .pdf ]
• Linear Factors [ .key ][ .pdf ]
• Autoencoders [ .key ][ .pdf ]
• Representation Learning [ .key ][ .pdf ]
• Structured Probabilistic Models for Deep Learning [ .key ][ .pdf ]
• Monte Carlo Methods [ .key ] [ .pdf ]
• Confronting the Partition Function [ .key ] [ .pdf ]

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an introduction to: Deep Learning

Jan 01, 2020

900 likes | 1.17k Views

an introduction to: Deep Learning. aka or related to Deep Neural Networks Deep Structural Learning Deep Belief Networks etc,. DL is providing breakthrough results in speech recognition and image classification …. From this Hinton et al 2012 paper:

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• deep learning
• random weights
• neural network
• al 2012 paper
• draw straight decision boundaries

Presentation Transcript

an introduction to: Deep Learning aka or related to Deep Neural Networks Deep Structural Learning Deep Belief Networks etc,

DL is providing breakthrough results in speech recognition and image classification … From this Hinton et al 2012 paper: http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/38131.pdf go here: http://yann.lecun.com/exdb/mnist/ From here: http://people.idsia.ch/~juergen/cvpr2012.pdf

So, 1. what exactly is deep learning ? And, 2. why is it generally better than other methods on image, speech and certain other types of data?

So, 1. what exactly is deep learning ? And, 2. why is it generally better than other methods on image, speech and certain other types of data? The short answers 1. ‘Deep Learning’ means using a neural network with several layers of nodesbetween input and output 2. the series of layers between input & output do feature identification and processing in a series of stages, just as our brains seem to.

hmmm… OK, but: 3. multilayerneural networks have been around for 25 years. What’s actually new?

hmmm… OK, but: 3. multilayerneural networks have been around for 25 years. What’s actually new? we have always had good algorithms for learning the weights in networks with 1 hidden layer but these algorithms are not good at learning the weights for networks with more hidden layers what’s new is: algorithms for training many-later networks

longer answers • reminder/quick-explanation of how neural network weights are learned; • the idea of unsupervised feature learning (why ‘intermediate features’ are important for difficult classification tasks, and how NNs seem to naturally learn them) • The ‘breakthrough’ – the simple trick for training Deep neural networks

-0.06 W1 W2 f(x) -2.5 W3 1.4

-0.06 2.7 -8.6 f(x) -2.5 0.002 x = -0.06×2.7 + 2.5×8.6 + 1.4×0.002 = 21.34 1.4

A dataset Fields class 1.4 2.7 1.9 0 3.8 3.4 3.2 0 6.4 2.8 1.7 1 4.1 0.1 0.2 0 etc …

Training the neural network Fields class 1.4 2.7 1.9 0 3.8 3.4 3.2 0 6.4 2.8 1.7 1 4.1 0.1 0.2 0 etc …

Training data Fields class 1.4 2.7 1.9 0 3.8 3.4 3.2 0 6.4 2.8 1.7 1 4.1 0.1 0.2 0 etc … Initialise with random weights

Training data Fields class 1.4 2.7 1.9 0 3.8 3.4 3.2 0 6.4 2.8 1.7 1 4.1 0.1 0.2 0 etc … Present a training pattern 1.4 2.7 1.9

Training data Fields class 1.4 2.7 1.9 0 3.8 3.4 3.2 0 6.4 2.8 1.7 1 4.1 0.1 0.2 0 etc … Feed it through to get output 1.4 2.7 0.8 1.9

Training data Fields class 1.4 2.7 1.9 0 3.8 3.4 3.2 0 6.4 2.8 1.7 1 4.1 0.1 0.2 0 etc … Compare with target output 1.4 2.7 0.8 0 1.9 error 0.8

Training data Fields class 1.4 2.7 1.9 0 3.8 3.4 3.2 0 6.4 2.8 1.7 1 4.1 0.1 0.2 0 etc … Adjust weights based on error 1.4 2.7 0.8 0 1.9 error 0.8

Training data Fields class 1.4 2.7 1.9 0 3.8 3.4 3.2 0 6.4 2.8 1.7 1 4.1 0.1 0.2 0 etc … Present a training pattern 6.4 2.8 1.7

Training data Fields class 1.4 2.7 1.9 0 3.8 3.4 3.2 0 6.4 2.8 1.7 1 4.1 0.1 0.2 0 etc … Feed it through to get output 6.4 2.8 0.9 1.7

Training data Fields class 1.4 2.7 1.9 0 3.8 3.4 3.2 0 6.4 2.8 1.7 1 4.1 0.1 0.2 0 etc … Compare with target output 6.4 2.8 0.9 1 1.7 error -0.1

Training data Fields class 1.4 2.7 1.9 0 3.8 3.4 3.2 0 6.4 2.8 1.7 1 4.1 0.1 0.2 0 etc … Adjust weights based on error 6.4 2.8 0.9 1 1.7 error -0.1

Training data Fields class 1.4 2.7 1.9 0 3.8 3.4 3.2 0 6.4 2.8 1.7 1 4.1 0.1 0.2 0 etc … And so on …. 6.4 2.8 0.9 1 1.7 error -0.1 Repeat this thousands, maybe millions of times – each time taking a random training instance, and making slight weight adjustments Algorithms for weight adjustment are designed to make changes that will reduce the error

The decision boundary perspective… Initial random weights

The decision boundary perspective… Present a training instance / adjust the weights

The decision boundary perspective… Eventually ….

The point I am trying to make • weight-learning algorithms for NNs are dumb • they work by making thousands and thousands of tiny adjustments, each making the network do better at the most recent pattern, but perhaps a little worse on many others • but, by dumb luck, eventually this tends to be good enough to learn effective classifiers for many real applications

Some other points Detail of a standard NN weight learning algorithm – later If f(x) is non-linear, a network with 1 hidden layer can, in theory, learn perfectly any classification problem. A set of weights exists that can produce the targets from the inputs. The problem is finding them.

Some other ‘by the way’ points If f(x) is linear, the NN can only draw straight decision boundaries (even if there are many layers of units)

Some other ‘by the way’ points NNs use nonlinear f(x) so they can draw complex boundaries, but keep the data unchanged

Some other ‘by the way’ points NNs use nonlinear f(x) so they SVMs only draw straight lines, can draw complex boundaries, but they transform the data first but keep the data unchanged in a way that makes that OK

Feature detectors

what is this unit doing?

Hidden layer units become self-organised feature detectors 1 5 10 15 20 25 … … 1 strong +ve weight low/zero weight 63

What does this unit detect? 1 5 10 15 20 25 … … 1 strong +ve weight low/zero weight 63

What does this unit detect? 1 5 10 15 20 25 … … 1 strong +ve weight low/zero weight it will send strong signal for a horizontal line in the top row, ignoring everywhere else 63

What does this unit detect? 1 5 10 15 20 25 … … 1 strong +ve weight low/zero weight Strong signal for a dark area in the top left corner 63

What features might you expect a good NN to learn, when trained with data like this?

vertical lines 1 63

Horizontal lines 1 63

Small circles 1 63

Small circles 1 But what about position invariance ??? our example unit detectors were tied to specific parts of the image 63

successive layers can learn higher-level features … etc … detect lines in Specific positions Higher level detetors ( horizontal line, “RHS vertical lune” “upper loop”, etc… etc … v

successive layers can learn higher-level features … etc … detect lines in Specific positions Higher level detetors ( horizontal line, “RHS vertical lune” “upper loop”, etc… etc … v What does this unit detect?

So: multiple layers make sense

So: multiple layers make sense Your brain works that way

So: multiple layers make sense Many-layer neural network architectures should be capable of learning the true underlying features and ‘feature logic’, and therefore generalise very well …

But, until very recently, our weight-learning algorithms simply did not work on multi-layer architectures

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