This is a slide deck from a presentation, that my colleague Uwe Friedrichsen (https://www.slideshare.net/ufried/) and I did together. As we created our respective parts of the presentation on our own, it is quite easy to figure out who did which part of the presentation as the two slide decks look quite different ... :)
For the sake of simplicity and completeness, Uwe copied the two slide decks together. As he did the "surrounding" part, he added my part at the place where I took over and then added concluding slides at the end. Well, I'm sure, you will figure it out easily ... ;)
The presentation was intended to be an introduction to deep learning (DL) for people who are new to the topic. It starts with some DL success stories as motivation. Then a quick classification and a bit of history follows before the "how" part starts.
The first part of the "how" is some theory of DL, to demystify the topic and explain and connect some of the most important terms on the one hand, but also to give an idea of the broadness of the topic on the other hand.
After that the second part dives deeper into the question how to actually implement DL networks. This part starts with coding it all on your own and then moves on to less coding step by step, depending on where you want to start.
The presentation ends with some pitfalls and challenges that you should have in mind if you want to dive deeper into DL - plus the invitation to become part of it.
As always the voice track of the presentation is missing. I hope that the slides are of some use for you, though.
18. Traditional AI
Focus on problems that are ...
• ... hard for humans
• ... straightforward for computers
• ... can be formally described
Deep Learning
Focus on problems that are ...
• ... intuitive for humans
• ... difficult for computers
(hard to be described formally)
• ... best learnt from experience
20. General evolution
• Two opposed forces
• Recreation of biological neural processing
• Abstract mathematical models (mostly linear algebra)
• Results in different models and algorithms
• No clear winner yet
21. Cybernetics (ca. 1940 - 1960)
• ADALINE, Perceptron
• Linear models, typically no hidden layers
• Stochastic Gradient Descent (SGD)
• Limited applicability
• E.g., ADALINE could not learn XOR
• Resulted in “First winter of ANN” (Artificial Neural Networks)
22. Connectionism (ca. 1980 - 1990)
• Neocognitron
• Non-linear models, distributed feature representation
• Backpropagation
• Typically 1, rarely more hidden layers
• First approaches of sequence modeling
• LSTM (Long short-term memory) in 1997
• Unrealistic expectations nurtured by ventures
• Resulted in “Second winter of ANN”
23. Deep Learning (ca. 2006 -)
• Improved algorithms, advanced computing power
• Enabled training much larger and deeper networks
• Enabled training much larger data sets
• Typically several to many hidden layers
• Overcame the “feature extraction dilemma”
25. Deep Learning application areas
• Classification (incl. missing inputs)
• Regression (value prediction)
• Function prediction
• Density estimation
• Structured output (e.g., translation)
• Anomaly detection
• Synthesis and sampling
• Denoising
• Compression (dimension reduction)
• ...
26. How does Deep Learning work?
A first (scientifically inspired) approach
27. „A computer program is said to learn
• from experience E
• with respect to some class of tasks T
• and performance measure P
if its performance at tasks in T,
as measured by P,
improves with experience E.”
-- T. Mitchell, Machine Learning, p. 2, McGraw Hill (1997)
Supervised learning,
unsupervised learning,
reinforcement learning, ...
Too difficult to solve
with fixed programs
designed by humans
Accuracy vs. error rate,
training vs. test set, ...
36. Neuron
• Design inspired by biological neurons
• One or more inputs
• Processing (and state storage) unit
• One or more outputs
• In practice often implemented as tensor transformations
• Relevance of internal state depends on network type
• Usually negligible for feed-forward networks
• Usually relevant for recurrent networks
Neuron
Processing
(+ State)
Output(s)
Input(s)
...
...
38. Layer
• Neurons typically organized in layers
• Input and output layer as default
• Optionally one or more hidden layer
• Layer layout can have 1-n dimensions
• Neurons in different layers can have different properties
• Different layers responsible for different (sub-)tasks
Output layer
Input layer
...
N
1
2
Hidden layer(s)
...
40. Connection
• Usually connect input and output tensor in a 1:1 manner
• Connect between layers (output layer N-1 à input layer N)
• Layers can be fully or partially (sparsely) connected
• RNNs also have backward and/or self connections
• Some networks have connections between neurons
of the same layer (e.g., Hopfield nets, Boltzmann machines)
Input tensor(s)
Output tensor(s)
42. Weight
• (Logically) augments a connection
• Used to amplify or dampen a signal sent over a connection
• The actual “memory” of the network
• The “right” values of the weights are learned during training
• Can also be used to introduce a bias for a neuron
• By connecting it to an extra neuron that constantly emits 1
W
Weight
44. Input tensor(s)
Output tensor(s)
Step 1
• For each neuron of input layer
• Copy resp. input tensor’s value to neuron’s input
• Calculate state/output using activation function
(typically linear function, passing value through)
Step 2-N
• For each hidden layer and output layer in their order
• For each neuron of the layer
• Calculate weighted sum on inputs
• Calculate state/output using activation function
(see examples later)
Final step
• For each neuron of output layer
• Copy neuron’s output to resp. output tensor’s value
45. Input tensor(s)
Output tensor(s)
Step 1
Final step
Step 2-N
• Default update procedure (most widespread)
• All neuron per layer in parallel
• Different update procedures exist
• E.g., some Hopfield net implementations
randomly pick neurons for update
53. Hyperparameter
• Influence network and algorithm behavior
• Often influence model capacity
• Not learned, but usually manually optimized
• Currently quite some research interest in
automatic hyperparameter optimization
Examples
• Number of hidden layers
• Number of hidden units
• Learning rate
• Number of clusters
• Weight decay coefficient
• Convolution kernel width
• ...
57. Cost function (a.k.a. loss function)
• Determines distance from optimal performance
• Mean squared error as simple (and widespread) example
58. Cost function (a.k.a. loss function)
• Determines distance from optimal performance
• Mean squared error as simple (and widespread) example
• Often augmented with regularization term
for better generalization (see challenges)
61. Stochastic gradient descent
• Direct calculation of minimum often not feasible
• Instead stepwise “descent” using the gradient
à Gradient descent
62. Stochastic gradient descent
• Direct calculation of minimum often not feasible
• Instead stepwise “descent” using the gradient
à Gradient descent
• Not feasible for large training sets
• Use (small) random sample of training set per iteration
à Stochastic gradient descent (SGD)
66. Backpropagation
• Procedure to calculate new weights based on loss function
Depends on
cost function
Depends on
activation function
Depends on
input calculation
67. Backpropagation
• Procedure to calculate new weights based on loss function
• Usually “back-propagated” layer-wise
• Most widespread optimization procedure
Depends on
cost function
Depends on
activation function
Depends on
input calculation
70. Data set
• Consists of examples (a.k.a. data points)
• Example always contains input tensor
• Sometimes also contains expected output tensor
(depending on training type)
• Data set usually split up in several parts
• Training set – optimize accuracy (always used)
• Test set – test generalization (often used)
• Validation set – tune hyperparameters (sometimes used)
73. Supervised learning
• Typically learns from a large, yet finite set of examples
• Examples consist of input and output tensor
• Output tensor describes desired output
• Output tensor also called label or target
• Typical application areas
• Classification
• Regression and function prediction
• Structured output problems
75. Unsupervised learning
• Typically learns from a large, yet finite set of examples
• Examples consist of input tensor only
• Learning algorithm tries to learn useful properties of the data
• Requires different type of cost functions
• Typical application areas
• Clustering, density estimations
• Denoising, compression (dimension reduction)
• Synthesis and sampling
77. Reinforcement learning
• Continuously optimizes interaction with an environment
based on reward-based learning
Agent
Environment
State t
Reward t
State t+1
Reward t+1
Action t
78. Reinforcement learning
• Continuously optimizes interaction with an environment
based on reward-based learning
• Goal is selection of action with highest expected reward
• Takes (discounted) expected future rewards into account
• Labeling of examples replaced by reward function
• Can continuously learn à data set can be infinite
• Typically used to solve complex tasks in (increasingly)
complex environments with (very) limited feedback
81. Underfitting and Overfitting
• Training error describes how good training data is learnt
• Test error is an indicator for generalization capability
• Core challenge for all machine learning type algorithms
1. Make training error small
2. Make gap between training and test error small
• Underfitting is the violation of #1
• Overfitting is the violation of #2
83. Underfitting and Overfitting
• Under- and overfitting influenced by model capacity
• Too low capacity usually leads to underfitting
• Too high capacity usually leads to overfitting
• Finding the right capacity is a challenge
85. Regularization
• Regularization is a modification applied to learning algorithm
• to reduce the generalization error
• but not the training error
• Weight decay is a typical regularization measure
87. Transfer learning
• How to transfer insights between related tasks
• E.g., is it possible to transfer knowledge gained while training
to recognize cars on the problem of recognizing trucks?
• General machine learning problem
• Subject of many research activities
92. Convolutional neural network (CNN)
• Special type of MLP for image processing
• Connects convolutional neuron only with receptive field
• Advantages
• Less computing
power required
• Often even better
recognition rates
• Inspired by organization of visual cortex
Image source: https://deeplearning4j.org
94. Recurrent neural network (RNN)
• Implements internal feedback loops
• Provides a temporal memory
• Typically used for
• Speech recognition
• Text recognition
• Time series processing
Image source: https://deeplearning4j.org
96. Long short-term memory (LSTM)
• Special type of RNN
• Uses special LSTM units
• Can implement very long-term memory
while avoiding the vanishing/exploding
gradient problem
• Same application areas as RNN
Image source: https://deeplearning4j.org
98. Autoencoder
• Special type of MLP
• Reproduces input at output layer
• Consists of encoder and decoder
• Usually configured undercomplete
• Learns efficient feature codings
• Dimension reduction (incl. compression)
• Denoising
• Usually needs pre-training for not only
reconstructing average of training set
Image source: https://deeplearning4j.org
100. Generative adversarial networks (GAN)
• Consists of two (adversarial) networks
• Generator creating fake images
• Discriminator trying to identify
fake images
• Typically used for
• Synthesis and sampling
(e.g., textures in games)
• Structured output with variance (e.g., variations of a design or voice generation)
• Probably best known for creating fake celebrity images
Image source: https://deeplearning4j.org
133. Issues you might face
• Very fast moving research domain
• You need the math. Really!
• How much data do you have?
• GDPR: Can you explain the decision of your network?
• Meta-Learning as the next step
• Monopolization of research and knowledge
135. Wrap-up
• Broad, diverse topic
• Very good library support and more
• Very active research topic
• No free lunch
• You need the math!
à Exciting and important topic – become a part of it!
136. References
• I. Goodfellow, Y. Bengio, A. Courville, ”Deep learning",
MIT press, 2016, also available via https://www.deeplearningbook.org
• C. Perez, “The Deep Learning AI Playbook”,
Intuition Machine Inc., 2017
• F. Chollet, "Deep Learning with Python",
Manning Publications, 2017
• OpenAI, https://openai.com
• Keras, https://keras.io
• Deep Learning for Java, https://deeplearning4j.org/index.html
• Deep Learning (Resource site), http://deeplearning.net
