ODSC March Meetup

1. Deep Learning - Concepts & Frameworks
Peter Morgan – Data Science Partnership
Deep Learning Frameworks ODSC Meetup Peter Morgan 16 Mar 2016 1

2. Contents
• Deep Learning Concepts ………….3
• Deep Learning Frameworks …..26
• Specific Frameworks ………..……30
• Comparison …………………………..33
• Questions ………………………………34
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3. Overview - Concepts not Code
No Maths No Code
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4. Frameworks - The Big Picture
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AI Frameworks
Cognitive
Architectures
ML Frameworks
Supervised,
Unsupervised &
Reinforcement
Deep Learning
Frameworks
Neural Nets

5. What is Deep learning?
• Deep learning refers to algorithms based on artificial neural networks (ANNs),
which in turn are based on biological neural networks (BNN), such as the human
brain.
• In practice it consists of mulitple layers, nodes, weights and optimisation
algorithms
• Due to more labeled data, more compute power, better optimization algorithms,
and better neural net models and architectures, deep learning has started to
supersede humans when it comes to image recognition and classification.
• Work is being done to obtain similar levels of performance in natural language
processing and understanding.
• Deep learning applies to supervised, unsupervised and reinforcement learning.
• According to Jeff Dean in a recent interview, Google have implemented DL in
over one hundred of their products and services including search and photos.
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6. Deep Learning Evolution
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7. Deep Learning Concepts
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8. Data Sets
• Raw data input into the neural network can originate from any environmental source.
• It can be recorded and stored in a database (e.g., text, images, audio, video) or live
(incident directly from the environment), so called streaming data.
• Examples of recorded data sets include the MNIST and Labeled Faces in the Wild
(LFW).
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MNIST LFW

9. Multilayer Perceptron (MLP)
• A MLP is a feedforward artificial neural network model that maps sets of input data onto a set of
appropriate outputs (Rosenblatt, 1958).
• An MLP consists of multiple layers of nodes with each layer fully connected to the next one.
• Except for the input nodes, each node is a neuron (or processing element) with a nonlinear
activation function.
• MLP utilizes a supervised learning technique called backpropagation for training the network.
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10. Layers and Nodes
• Layers
• A neural network is made up of layers of nodes.
• There is an input (visible) layer, an output (classification) layer, and several hidden layers.
• A typical NN may have between 10 and 30 layers, but sometimes many more.
• Every layer of a deep learning network requires four elements: the input (vector), the weights
(matrix), a bias and the transform (activation function).
• Nodes
• Nodes in a neural network represent the places where calculations are done.
• At each node, the input values xi are multiplied by a weight wi, summed, added to a bias b, and
fed into an activation function.
• A decision is then made whether to transmit the resultant value depending on if it exceeds a
certain threshold value or not.
• For example, images from the MNIST data set have 784 pixels, so neural nets processing them
must have 784 input nodes, one per pixel.
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11. Weights & Softmax
• Weights
• Coefficients that amplify or mute the input signal coming into each node.
• They are assigned initial values which can be random or chosen based
upon some insight.
• A NN can be represented by its weight matrix along with its activation
functions.
• Softmax
• The softmax function, or normalized exponential, is a generalization of the
sigmoid logistic function.
• In neural network simulations, the softmax function is often implemented
at the final layer of a network used for classification.
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12. Activation Function
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• One of a set of functions that determine the threshold at each node above which a
signal is passed through the node, and below which it is blocked.
• Activation functions normalize input from a previous layer to a value between -1
and 1 to ensure an output layer probability of between 0 and 1.
• Functions that achieve this include the logistic, sigmoid, tanh and ReLU functions.

13. Optimization and Overfitting
• Optimization
• Refers to the manner by which a neural net minimizes error as it
adjusts its coefficients (weights) step by step.
• L-BFGS is one such algorithm.
• Overfitting
• Overfitting is where too many parameters are used in the model
constructed to fit the data which leads to poor predictive power of
the model.
• Regularisation, cross-validation and dropout are all methods used to
address this problem.
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14. Regularisation and Cross-validation
• Both techniques are used to prevent overfitting
• Regularisation
• Regularisation refers to a process of introducing additional information in order to
prevent overfitting.
• It penalizes models with extreme parameter values by introducing a factor which weights
the penalty against more complex models with an increasing variance in the data errors.
• Cross-validation
• Cross-validation combines sampling sets to correct for overfitting and derive a more
accurate estimate of model prediction performance.
• One round of cross-validation involves partitioning a sample of data into complementary
subsets, performing the analysis on one subset (called the training set), and validating
the analysis on the other subset (called the validation set or testing set).
• To reduce variability, multiple rounds of cross-validation are performed using different
partitions, and the validation results are averaged over the rounds.
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15. Feed Forward Networks
• The feedforward neural network was the first and simplest type of artificial neural
network devised.
• In this network, the information moves in only one direction, forward, from the
input nodes, through the hidden nodes and to the output nodes.
• There are no cycles or feedback loops in the network.
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16. Support Vector Machines (SVM)
• Support vector machines are supervised learning algorithms that analyze data
and recognize patterns, in order to classify data points (Vapnik, 1979).
• Given a set of training examples, each marked for belonging to one of two
categories, an SVM training algorithm builds a model that assigns new examples
into one category or the other – it is a linear classifier.
• Recently SVM’s have been usurped by the success of deep learning algorithms.
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17. Stochastic Gradient Descent
• Stochastic gradient descent is a popular algorithm for training a wide range of models
in machine learning, including support vector machines and logistic regression.
• When combined with the backpropagation algorithm, it is the de facto standard
algorithm for training artificial neural networks.
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18. Back Propagation
• Backpropagation is a common method of training artificial neural networks used in conjunction
with an optimization method such as gradient descent.
• The method calculates the gradient of a loss function with respect to all the weights in the
network.
• The gradient is fed to the optimization method which in turn uses it to update the weights, in an
attempt to minimize the loss function.
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19. Markov Models
• Markov Model
• A Markov model is a stochastic (probabilistic) model used to model
randomly changing systems where it is assumed that future states depend
only on the present state and not on the sequence of events that preceded
it.
• Markov Chain
• Markov Chains are essentially logical circuits that connect two or more
states via probabilities.
• Markov Chains are sequential. Their purpose is to give you a good idea,
given one state, of what the next state will be.
• Use cases include natural language processing (NLP) and stock price
trading.
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20. Hidden Markov Model
• An HMM is a statistical Markov model in which the system being modeled is
assumed to be a Markov process with unobserved (hidden) states.
• A HMM can be presented as the simplest dynamic Bayesian network.
• They are used in time series prediction, e.g., in language analysis.
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21. Restricted Boltzmann Machines & Autoencoders
• Restricted Boltzmann Machines (RBM)
• Invented by Geoff Hinton (1999) at the University of Toronto, RBMs are shallow, two-
layer neural nets consisting of an input layer and a hidden layer.
• They constitute the building blocks of deep neural networks - a deep learning network is
simply many restricted Boltzmann machines stacked on top of one another.
• Nodes are connected to each other across layers, but have the restriction that no two
nodes of the same layer are linked.
• Autoencoder
• An autoencoder is an ANN used for learning efficient codings.
• The aim of an autoencoder is to learn a compressed, distributed representation
(encoding) for a set of data, typically for the purpose of dimensionality reduction.
• In an autoencoder, the output layer has equally many nodes as the input layer, and
instead of training it to predict some target value y given inputs x, an autoencoder is
trained to reconstruct its own inputs x.
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22. Max Pooling and Cost Functions
• Max Pooling
• In CNN’s, max-pooling partitions the input image into a set of non-overlapping rectangles and, for
each such sub-region, outputs the maximum value.
• Loss Function
• A loss function or cost function is a function that maps an event or values of one or more variables
onto a real number intuitively representing some "cost" associated with the event.
• The opposite of a loss function is called a reward function, or utility function.
• Dropout
The dropout method is introduced to prevent overfitting.
• At each training stage, individual nodes are either "dropped out" of the net with probability 1-p or
kept with probability p, so that a reduced network is left.
• By avoiding training all nodes on all the training data, dropout decreases overfitting in neural nets.
• The method also significantly improves the speed of training.
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23. Convolutional Neural Networks
• First developed in 1970’s.
• Widely used for image recognition and classification.
• Inspired by biological processes, CNN’s are a type of feed-forward ANN.
• The individual neurons are tiled in such a way that they respond to overlapping
regions in the visual field.
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24. Recurrent Neural Networks
• First developed in 1970’s.
• RNN’s are neural networks that are used to predict the next element in a
sequence or time series.
• This could be, for example, words in a sentence or letters in a word.
• Applications include predicting or generating music, stories, news, code, financial
instrument pricing, text, speech, in fact the next element in any event stream.
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25. LSTM and NTM
• Long Short Term Memory (LSTM)
• LSTM is an RNN architecture that contains blocks that can remember a value for an
arbitrary length of time.
• It solves the vanishing or exploding gradient problem when calculating back propagation.
• An LSTM network is universal in the sense that given enough network units it can
compute anything a conventional computer can compute, provided it has the proper
weight matrix.
• LSTM outperforms alternative RNNs and Hidden Markov Models and other sequence
learning methods in numerous applications, e.g., in handwriting recognition, speech
recognition and music composition.
• Neural Turing Machines (NTM)
• NTMs are a method of extending the capabilities of recurrent neural networks by
coupling them to external memory resources.
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26. Deep Learning Frameworks
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Framework = Toolkit = Library

27. Deep Learning Frameworks
• Apache SINGA
• Brainstorm
• Caffe
• Chainer
• CNTK (Microsoft)
• DL4J
• DMLC
• Fbcunn (Facebook)
• Lasagne
• Minerva
• Mocha.jl (Julia)
• MXnet
• Neon (Nervana)
• Purine
• Tensorflow (Google)
• Theano
• Torch
• Warp-CTC (Baidu)
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28. Deep Learning Frameworks (cont)
• Brain (Javascript)
• Cudamat
• Deep Learning Framework (Intel)
• Deepnet
• Hebel
• Infer.NET
• Keras
• Leaf
• MLPNeuralNet
• Neural Network Toolbox
(MatLab)
• Neuraltalk
• Neurolab
• OpenDeep
• PyBrain
• Swift-AI
• VELES (Samsung)
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29. Some Specific DL Libraries
• ANN
• Brainstorm
• clab/cnn
• DeepHear (music composition)
• DoctorTeeth/diffmem
• miloharper/neural-network-animation
• CNN
• ConvNetJS
• Marvin
• MatConvNet
• RNN
• awesome-rnn
• karpathy/char-rnn
• karpathy/neuraltalk2
• wojciechz/learning_to_execute
• LSTM
• dl4j-0.4 Graves LTSM
• github.nicodjimenez/lstm
• Russell91/LSTMSummation
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30. TensorFlow
• TensorFlow is the newly (Nov 2015) open sourced deep learning library
from Google.
• It is their second generation system for the implementation and
deployment of large-scale machine learning models.
• Written in C++ with a python interface, it is borne from research and
deploying machine learning projects throughout a wide range of
Google products and services.
• Initially TF ran only on a single node (your laptop, say), but Google have
recently released a version that now runs on a cluster.
• https://www.tensorflow.org/
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31. Torch
• First released in 2000, with over 50,000 downloads, company users
include Google, Facebook, Twitter.
• The goal of Torch is to have maximum flexibility and speed in building
scientific algorithms while making the process extremely simple.
• Torch is a neural network library written in Lua with a C/CUDA
interface originally developed by a team from the Swiss institute EPFL.
• At the heart of Torch are popular neural network and optimization
libraries which are simple to use, while being flexible in implementing
different complex neural network topologies.
• http://torch.ch/
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32. Theano
• Theano is a deep learning library written in python and popular for its
ease of use.
• Using Theano, it is possible to attain speeds rivalling hand-crafted C
implementations for problems involving large amounts of data.
• Theano has been powering large-scale computationally intensive
scientific investigations since 2007
• Supports CuDNN v3
• http://deeplearning.net/software/theano/
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33. DL Framework Comparison
• The most common metrics we can use to compare these deep learning frameworks are:
• Speed of execution
• Ease of use
• Languages used (core and front end)
• Resources (CPU and memory capacity) needed in order to run the various algorithms
• GPU support
• Size of active community of users
• Contributors and committers
• Platforms supported (e.g., OS, single devices and/or distributed systems)
• Algorithmic support
• Number of packages in their library
• For example, various benchmarks and comparisons are available here:
https://github.com/soumith/convnet-benchmarks
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34. Questions?
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