Mini-course on Artificial Neural Networks and Bayesian Networks Michal Rosen-Zvi Mini-course on ANN...

Mini-course on Artificial Neural Networks and Bayesian Networks

Michal Rosen-Zvi

Mini-course on ANN and BN, The Multidisciplinary Brain Research center, Bar-Ilan University, May 2004

קורס מרוכז תחומי הרב ברשתות המרכז לחקר המוח אוניברסיטת בר־ אילן תשס״ד

Section 1: Introduction



Networks (1)

Networks serve as a visual way for displaying relationships:

Social networks are examples of ‘flat’ networks where the only information is relation between entities



Example: collaboration network



1. Analyzing Cortical Activity using Hidden Markov Models

Itay Gat, Naftali Tishby, and Moshe Abeles"Network, Computation in Neural Systems", August 1997.2. Cortical Activity Flips Among Quasi Stationary StatesMoshe Abeles, Hagai Bergman, Itay Gat, Isaac Meilijson, Eyal Seidemann, Naftali Tishby, Eilon VaadiaPrepared: Feb 1, 1995, Appeared in the Proceedings of the National Academy of Science (PNAS)3. Rigorous Learning Curve Bounds from Statistical Mechanics

David Haussler, Michael Kearns, H. Sebastian Seung, and Naftali TishbyPrepared: July 1994. Full version, Machine Learning (1997).

4. H. S. Seung, Haim Sompolinsky, Naftali Tishby: Learning Curves in Large Neural Networks. COLT 1991: 112-127

5. Yann LeCun, Ido Kanter, Sara A. Solla: Second Order Properties of Error Surfaces. NIPS 1990: 918-924

6. Esther Levin, Naftali Tishby, Sara A. Solla: A Statistical Approach to Learning and Generalization in Layered Neural Networks. COLT 1989: 245-260

7. Litvak V, Sompolinsky H, Segev I, and Abeles M (2003) On the Transmission of Rate Code in Long Feedforward Networks with Excitatory-Inhibitory Balance. Journal of Neuroscience, 23(7):3006-30158. Senn, W., Segev, I., and Tsodyks, M. (1998). Reading neural synchrony with depressing synapses. Neural Computation 10: 815-819

8. Tsodkys, M., I.Mit'kov, H.Sompolinsky (1993): Pattern of synchrony in inhomogeneous networks of oscillators with pulse interactions. Phys. Rev. Lett.,

9. Memory Capacity of Balanced Networks (Yuval Aviel, David Horn and Moshe Abeles)10. The Role of Inhibition in an Associative Memory Model of the Olfactory Bulb.

(Ofer Hendin, David Horn and Misha Tsodyks)11 Information Bottleneck for Gaussian Variables

Gal Chechik, Amir Globerson, Naftali Tishby and Yair WeissPrepared: June 2003. Submitted to NIPS-2003

[matlab]

Networks (2)

Artificial Neural Networks represent rules – deterministic relations - between input and output



Networks (3)

Bayesian Networks represent probabilistic relations - conditional independencies and dependencies between variables



Outline

Introduction/Motivation Artificial Neural Networks

– The Perceptron, multilayered FF NN and recurrent NN– On-line (supervised) learning– Unsupervised learning and PCA– Classification– Capacity of networks

Bayesian networks (BN)– Bayes rules and the BN semantics– Classification using Generative models

Applications: Vision, Text



Motivation

The research of ANNs is inspired by neurons in the brain and (partially) driven by the need for models of the reasoning in the brain.

Scientists are challenged to use machines more effectively for tasks traditionally solved by humans (example - driving a car, inferring scientific referees to papers and many others)



Questions

How can a network learn? What will be the learning rate? What are the limitations on the network capacity? How networks can be used to classify results with no

labels (unsupervised learning)? What are the relations and differences between

learning in ANN and learning in BN? How can network models explain high-level

reasoning?



History of (modern) ANNs and BNs


1940 1950 1960 1970 1980 1990 2000

McCulloch and Pitts Model

Hebbian Learning rule

Minsky and Papert’s book

Perceptron Hopfield Network

Pearl’s Book

Statistical Physics


Section 2: On-line Learning


Based on slides from Michael Biehl’s summer course


Section 2.1: The Perceptron



The Perceptron

Input:

Adaptive Weights JJ

Output: SMini-course on ANN and BN, The Multidisciplinary Brain Research center, Bar-Ilan University, May 2004


Perceptron: binary output

Implements a linearly separable classification of inputs

Milestones:Perceptron convergence theorem, Rosenblatt

(1958)Capacity, winder (1963) Cover(1965)Statistical Physics of perceptron weights,

Gardner (1988)

How does this device learn?


W


Learning a linearly separable rule from reliable examples

Unknown rule: ST()=sign(BB) =±1

Defines the correct classification.

Parameterized through a teacher perceptron with weights BBRN, (BBBB=1)

Only available information: example data

D= { , ST()=sign(BB) for =1…P }



Learning a linearly… (Cont.)

Training: finding the student weights JJ– J J parameterizes a hypothesis SS()=sign(JJ) – Supervised learning is based on the student

performance with respect to the training data DD– Binary error measure

T(JJ)= [S

S(),ST()]

T(JJ)=1 if S

S()ST()

T(WW)=0 if SS()=S

T()



Off-line learning

Guided by the minimization of a cost function H(JJ), e.g., the training error

H(JJ) tT(JJ)

Equilibrium statistical mechanics treatment:– Energy H of N degrees of freedm– Ensemble of systems is in thermal equilibrium at formal

temperature– Disorder avg. over random examples (replicas) assumes

distribution over the inputs– Macroscopic description, order parameters– Typical properties of large sustems, P= N



On-line training

Single presentation of uncorrelated (new) {,S

T()} Update of student weights:

Learning dynamics in discrete time



On-line training - Statistical Physics approach

Consider sequence of independent, random Thermodynamic limit Disorder average over latest example self-

averaging properties Continuous time limit



Generalization

Performance of the student (after training) with respect to arbitrary, new input

In practice: empirical mean of mean error measure over a set of test inputs

In the theoretical analysis: average over the (assumed) probability density of inputs

Generalization error:



Generalization (cont.)

The simplest model distribution:

Isotropic density P(), uncorrelated with B B and JJ

Consider vectors of independent identically distributed (iid) components jj with



Geometric argument

Projection of data into (BB, JJ)-plane yields isotropic density of inputs


BBJJ

ST()=SS()

g=/

For |BB|=1


Overlap Parameters

Sufficient to quantify the success of learning

R=BBJ J Q=JJJ J

Random guessing R=0, g=1/2

Perfect generalization , g=0



Derivation for large N

Given BB, JJ, and uncorrelated random input i=0, i j =ij, consider student/teacher fields that are sums of (many) independent random quantities:

x=JJ=∑iJiI

y=BB=∑iBii



Central Limit Theorem

Joint density of (x,y) is for N→∞, a two dimensional Gaussian, fully specified by the first and the second moments

x=∑iJii=0 y=∑iBii=0

x2 = ∑ijJiJjij = ∑iJi2 = Q

y2 = ∑ijBiBjij = ∑iBi2 = 1

xy = ∑ijJiBjij = ∑iJiBi = R



Central Limit Theorem (Cont.)

Details of the input are irrelevant.

Some possible examples: binary, i1, with equal prob. Uniform, Gaussian.



Generalization Error

The isotropic distribution is also assumed to describe the statistics of the example data inputs



Exercise: Derive the generalization error as a function of R,Q use Mathematical notes

Assumptions about the data

No spatial correlatins No distinguished directions in the input space No temporal correlations No correlations with the rule Single presentation without repeatitionsConsequences: Average over data can be performed step by step Actual choice of B B is irrelevant, it is not necessary to

averaged over the teacher



Hebbian learning (revisited) Hebb 1949

Off-line interpretation Vallet 1989

Choice of student weights given D={,ST}=1

P

JJ(P) = ∑ST/N

Equivalent On-line interpretation

Dynamics upon single presentation of examples

JJ() = JJ(-1) + ST/N



Hebb: on-line

From microscopic to macroscopic: recursions for overlaps



Exercise: Derive the update equations of R,Q

Hebb: on-line (Cont.)

Average over the latest example …

The random input, enters only through the fields

The random input and JJ(-1), BB are statistically independent

The Central Limit Theorems applies and obtains the joint density






Exercise: Derive the update equations of R,Q as a function of use Mathematical notes [off-line]


Continuous time limit, N→∞, = /N, d=1/N



Initial conditions - tabula rasa R(0)=Q(0)=0

What are the mean values after training with N examples???

[See matlab code]

Hebb: on-line mean values

The order parameters, Q and R, are self averagingself averaging for infinite N

Self average properties of A(JJ):– The observation of a value of A different from its mean

occurs with vanishing probability



Learning Curve: dependent of the order parameters



Exercise: Solve the differential equations for R and Q

Exercise: Find the function ()




The normalized overlap between the two vectors, BB, J J provides the angle between the vectors two vectors

1cos1 g




Exercise: Find asymptotic behavior of ()

Asymptotic expansion [draw w. matlab]



Questions:

What are other learning algorithms that can be used for efficient learning?

What training algorithm will provide the best learning/ the fastest asymptotic decrease?



Modified Hebbian learning

The training algorithm is defined by a modulation function f

JJ() = JJ(-1) +f(…) ST/N

Restriction: f may depend on available quantities: f(JJ(-1),,S

T)



Perceptron Rosenblatt 1959



If classification is correct don’t change the weights.

If classification is incorrect

– if the right class for the

example is 1 JJ(). increases.– if right class for the example

is -1 JJ(). decreases

w

Perceptron



Only informative points are used (mistake driven) The solution is a linear combination of the training

points Converges only for linearly separable data

Exercise: Derive the update equations of ,Q as a function of , J,B and

On-line dynamics Biehl and Riegler 1994



Questions:

Find the asymptotic behavior (by simulations and/or analytically) of the generalization error for the perceptron algorithm and Hebb algorithm, which one is better?

What training algorithm will provide the best learning/ the fastest asymptotic decrease?



Learning Curve - Hebb and Perceptron



Section 2.2: On-line by gradient descent



Introduction

Commonly used in practical applications:

Multilayered neural network with continuous activation functions, where output is a differentiable function of the adaptive parameters

Can be used for fitting a function to a data



Linear perceptron and linear regression (1D)

x=J

Using a quadratic loss function and gradient descent for finding the best curve to fit a data set [see ◘ , off-line]



y

Simple case: ‘Linear perceptron’

Teacher: ST()=y=BBStudent: SS()=x=JJ

Training and performance evaluation are based on the quadratic error



Consider the training dynamics

Exercise: Derive the update equations of R,Q as a function of

‘Linear perceptron’ (cont.)

Some exercises: Write a matlab code for the linear perceptron,

teacher-student scenario. Show that Investigate the role of the learning rate Find the asymptotic decrease to zero errors



Adatron: binary output JJ() = JJ(-1) +f(…) ST/N

Some exercises: Write a matlab code for the linear perceptron,

teacher-student scenario. Find the asymptotic decrease to zero errors Compare with the performance of the

Perceptron and Hebb rule



Multilayered feed-forward NN

Example architecture: the soft-committee machine



Multilayered ff NN (cont.)

Transfer function: sigmoidal g(x)

e.g., g(x)=tanh(x) or g(x)=

Error function is defined:

The total output:



Teacher-Student scenario

If teacher and student have the same architecture but student has K hidden units and teacher as M hidden units,

Can the student learn the rules?



K < M

Unlearnable rule

K = M

Learnable rule Overlearnable rule

K > M

In the following we will discuss matching architectures

The error measure

One (obvious) choice for continuous outputs



On-line gradient descent

Assuming the same learning rate, , over all the network, the update equations are for fixed known {v}



Assumptions and definitions

Isotropic uncorrelated input data The number of input components N is huge



The rule is specified by the norms of the teacher, say all 1s

Order parameters role

The set of order parameters and weights is sufficient for describing the learning, this is the macroscopic set of parameters



Microscopic: KN+K degrees of freedom

Macroscopic: K(K-1)/2+KM+K different order parameters

Generalization error: erf function Saad & Solla 1995

Reflect symmetries of the soft committee machine



Permutation Symmetry

The generalization error is characterized by invariance under permutations of branches

How do you think this feature affects learning performance?



A simple case

Hidden to output weights are fixed and known wi=vi=1

The update rule is



Update of the order parameters



Differential Equations



Learning curves



Section 3: Unsupervised learning


Based on slides from Michael Biehl’s summer course


Introduction

Learning without a teacher!?

Real world data is, in general, not isotropic and structure less in input space.

Unsupervised learning = extraction of information from unlabelled inputs



Potential aims

Correlation analysis Clustering of data – grouping according to

some similarity criterion Identification of prototypes – represent large

amount of data by few examples Dimension reduction – represent high

dimensional data by few relevant features



A simple example

Prototypes for high dimensional data – directions in the space

Assume data points are distributed as



A simple example (cont)

The student task is to find the directions BB11 and BB22 The data looks different in different planes!



Student scenario

Search for the two vectors, using a two student vectors:– Define set of possible learning rules– Analyze learning abilities– Compare and choose the best learning

It would provide the two principle principle componentscomponents of the data



PCA: General setting [matlab]



N

n

Tnn

N 1

))((1

xxxxΣ

Given a set of data points: X X

1. Compute the covariance matrix

2. Compute the eigenvalues and eigenvectors of the covariance matrix

3. Arrange the egienvalues from the biggest to the smallest. Take the first d eigenvectors as principle components if the input dimensionality is to be reduced to d.

4. Project the input data onto the principle components, which forms the representation of input data.

Principle Component Analysis

Algebraic view point: Given data find a linear transformation such that the sum of squared distances is minimized over all linear transformations

Statistical view point: Given data assume that each point is a random variable sampled from a Gaussian with unit covariance and mean. Find the ML estimator of the means under the constraint that there are K different means that are linearly related to the data.



Example: vision



Example: vision (cont)



Average results for each of the 6400 pixels

First nine eigen faces



Dimensionality Reduction

The goal is to compress information with minimal loss

Methods: – Unsupervised learning

Principle Component Analysis

– Nonnegative Matrix Factorization Bayesian Models (Matrices are probabilities)



Section 4: Bayesian Networks

Some slides are from Baldi’s course on Neural Networks



Bayesian Statistics

Bayesian framework for induction: we start with hypothesis space and wish to express relative preferences in terms of background information (the Cox-Jaynes axioms).

Axiom 0: Transitivity of preferences. Theorem 1: Preferences can be represented by a real number π (A). Axiom 1: There exists a function f such that

π(non A) = f(π(A)) Axiom 2: There exists a function F such that

π (A,B) = F(π(A), π(B|A)) Theorem2: There is always a rescaling w such that p(A)=w(π(A)) is in

[0,1], and satisfies the sum and product rules.



The Asia problem

“Shortness-of-breath (dyspnoea) may be due to Tuberculosis, Lung cancer or bronchitis, or none of them. A recent visit to Asia increases the chances of tuberculosis, while Smoking is known to be a risk factor for both lung cancer and Bronchitis. The results of a single chest X-ray do not discriminate between lung cancer and tuberculosis, as neither does the presence or absence of Dyspnoea.”


Lauritzen & Spiegelhalter 1988


Graphical models

“Successful marriage between Probabilistic Theory and Graph Theory”

M. I. Jordan


P(x1,x2,x3) P(x1,x3) P(x2,x3)

P(x1,x2,x3) (x1,x3) (x2,x3)x1

x3x2Applications: Vision, Speech

Recognition, Error correcting codes, Bioinformatics


Directed acyclic Graphs

Involves conditional dependencies


x1

x3x2

P(x1,x2,x3) = P(x1)P(x2)P(x3|x1,x2)


Directed Graphical Models (2)

Each node is associated with a random variable

Each arrow is associated with conditional dependencies (Parents–child)

Shaded nodes illustrates an observed variable

Plates stand for repetitions of i.i.d. drawings of the random variables



Classification problem

This is problem is ‘unsupervised’ where one is searching for best labels that fit the data, and does not have any examples that contain labels

Perceptron and Support vector machines are widely used for classifications. These are discriminative methods



Classification: assigning labels to data



Discrete Classifier, modeling the boundaries between different classes of the data

Prediction of Categorical output e.g., SVM

Density Estimator: modeling the distribution of the data points themselves

Generative Models e.g. NB

Density estimator

The simplest model for density estimation is the Naïve Bayes classifier



Assumes that each of the data points is distributed independently:

Results in a trivial learning algorithm

Usually does not suffer from overfitting

Directed graph: ‘real world’ example


x

z

w

D

A

T

da

NdThe author topic model

Statistical modeling of data mining:

Huge corpus, authors and words are observed, topics and relations are learned.


Goal

Automatically extract topical content of documents and learn association of topics to authors of documents

Expand existing probabilistic topic models to include author information

Some queries that model should be able to answer: – What topics does author X work on?– Which authors work on topic X? – What are interesting temporal patterns in topics?



Previous topic-based models

Hoffman (1999): Probabilistic Latent Semantic Indexing (pLSI)

– EM implementation– Problem of overfitting

Latent Dirichlet Allocation (LDA) Blei, Ng, & Jordan (2003): Griffiths

& Steyvers, (PNAS 2004): – Clarified the pLSI model– Variational EM, Scalability?– Gibbs sampling technique for inference

Computationally simple, Efficient (linear with size of data), Can easily be applied to >100K documents



Classification



Topics Model for Semantic Representation

Based on a Professor Mark Steyver’s slides, a joint work of Mark Steyver’s (UCI) and Tom Griffiths (Stanford)



The DRM Paradigm

The Deese (1959), Roediger, and McDermott (1995) Paradigm:

Subjects hear a series of word lists during the study phase, each comprising semantically related items strongly related to another non-presented word (“false target”).

Subjects (later) receive recognition tests for all words plus other distracted words including the false target.

DRM experiments routinely demonstrate that subjects claim to recognize false tagets.



Example: test of false memory effects in the DRM Paradaigm

STUDY: Bed, Rest, Awake, Tired, Dream, Wake, Snooze, Blanket, Doze, Slumber, Snore, Nap, Peace, Yawn, Drowsy

FALSE RECALL: “Sleep” 61%



A Rational Analysis of Semantic Memory

Our associative/semantic memory system might arise from the need to efficiently predict word usage with just a few basis functions (i.e., “concepts” or “topics”)

The topics model provides such a rational analysis



A Spatial Representation: Latent Semantic Analysis (Landauer & Dumais, 1997)


Document/Term count matrix

1

…

16

…

0

…

SCIENCE

…

6190RESEARCH

2012SOUL

3034LOVE

Doc3 … Doc2Doc1

High dimensional space

SOUL

RESEARCH

LOVE

SCIENCE

SVD

EACH WORD IS A SINGLE POINT IN A SEMANTIC SPACE


Triangle Inequality constraint on words with multiple meanings

Euclidian distance: AC AB + BC


FIELD MAGNETIC

SOCCER

AB

BC

AC


A generative model for topics

Each document (i.e. context)

is a mixture of topics.

Each topic is a distribution

over words.

Each word is chosen

from a single topic.


z

wN

D

T


A toy example



P( z = 1 ) P( z = 2 )

wi

P( w | z )SCIENTIFIC 0.4 KNOWLEDGE 0.2WORK 0.1RESEARCH 0.1MATHEMATICS 0.1MYSTERY 0.1

P( w | z )HEART 0.3 LOVE 0.2SOUL 0.2TEARS 0.1MYSTERY 0.1JOY 0.1

Words can occur in multiple topics

TOPIC MIXTURE

All probability to topic 1…



P( z = 1 )=1 P( z = 2 )=0

wi



One TOPIC

Document: HEART, LOVE, JOY, SOUL, HEART, ….

All probability to topic 2…



P( z = 1 )=0 P( z = 2 )=1

wi



Document: SCIENTIFIC, KNOWLEDGE, SCIENTIFIC, RESEARCH, ….

Application to corpus data

TASA corpus: text from first grade to college– representative sample of text

26,000+ word types (stop words removed) 37,000+ documents 6,000,000+ word tokens



Fitting the model

Learning is unsupervised Learning means inverting the generative model

– We estimate P( z | w ) – assign each word in the corpus to one of T topics

– With T=500 topics and 6x106 words, the size of the discrete state space is (500)6,000,000 HELP!

– Efficient sampling approach Markov Chain Monte Carlo (MCMC)

– Time & Memory requirements linear with T and N



Gibbs Sampling & MCMCsee Griffiths & Steyvers, 2003 for details

Assign every word in corpus to one of T topics

Sampling distribution for z:


number of times word w assigned to topic j

number of times topic j used in document d


A selection from 500 topics [P(w|z = j)]


THEORYSCIENTISTSEXPERIMENTOBSERVATIONSSCIENTIFICEXPERIMENTSHYPOTHESISEXPLAINSCIENTISTOBSERVEDEXPLANATIONBASEDOBSERVATIONIDEAEVIDENCETHEORIESBELIEVEDDISCOVERED

SPACEEARTHMOONPLANETROCKETMARSORBITASTRONAUTSFIRSTSPACECRAFTJUPITERSATELLITESATELLITESATMOSPHERESPACESHIPSURFACESCIENTISTSASTRONAUT

ARTPAINTARTISTPAINTINGPAINTEDARTISTSMUSEUMWORKPAINTINGSSTYLEPICTURESWORKSOWNSCULPTUREPAINTERARTSBEAUTIFULDESIGNS

BRAINNERVESENSESENSESARENERVOUSNERVESBODYSMELLTASTETOUCHMESSAGESIMPULSESCORDORGANSSPINALFIBERSSENSORY


Polysemy: words with multiple meanings represented in different topics


FIELDMAGNETIC

MAGNETWIRE

NEEDLECURRENT

COILPOLESIRON

COMPASSLINESCORE

ELECTRICDIRECTION

FORCEMAGNETS

BEMAGNETISM

SCIENCESTUDY

SCIENTISTSSCIENTIFIC

KNOWLEDGEWORK

RESEARCHCHEMISTRY

TECHNOLOGYMANY

MATHEMATICSBIOLOGY

FIELDPHYSICS

LABORATORYSTUDIESWORLD

SCIENTIST

BALLGAMETEAM

FOOTBALLBASEBALLPLAYERS

PLAYFIELD

PLAYERBASKETBALL

COACHPLAYEDPLAYING

HITTENNISTEAMSGAMESSPORTS

JOBWORKJOBS

CAREEREXPERIENCE

EMPLOYMENTOPPORTUNITIES

WORKINGTRAINING

SKILLSCAREERS

POSITIONSFIND

POSITIONFIELD

OCCUPATIONSREQUIRE

OPPORTUNITY


Predicting word association

LSA: finds the closest word

Topics Model: do inference – given that one word was observed what will be the next word with the highest probability?



Word Association (norms from Nelson et al. 1998)


Associate N. People:1 EARTH2 STARS3 SPACE4 SUN5 MARS

CUE: PLANET

Model STARS

SUN EARTH SPACE

SKY


First associate “EARTH” is in the set of 5 associates (from the model)

P( set contains first associate )



100

101

102

103

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

P(s

et

con

tain

s fir

st a

sso

cia

te)

Set size

LSATOPICS

Explaining variability in false recall

One factor: mean associative strength of list items to critical item (Deese 1959; Roediger et al. 2001).



BEDREST

AWAKETIRED

…DROWSY

.638

.475

.618

.493

.551

For 55 DRM lists, R = .69 (with the given lexicon)

Mean = .431

SLEEP

One recall component: inference

Encoding: study words lead to topics distribution (“gist”)

Retrieval: infer words from stored topics distribution



Predictions for the “Sleep” list



0 0.05 0.1 0.15 0.2 0.25

SLEEP

RESTTIRED

BEDWAKE

AWAKENAP

DREAMYAWNDROWSYBLANKETSNORESLUMBERDOZEPEACE

SLEEPNIGHT

HOURSMORNING

ASLEEPSLEEPYAWAKENEDSPENT

STUDYLIST

EXTRALIST

(top 8)

studyrecallwP w|

Correlation between intrusion rates and predictions



LSA

# Dimensions

0 200 400 600 800

Cor

rela

tion

0.2

0.3

0.4

0.5

0.6

0.7

0.8

TOPICS MODEL

# Topics

0 400 800 1200 1600 2000

Cor

rela

tion

0.2

0.3

0.4

0.5

0.6

0.7

0.8

(word association) (word association)

Other recall components??? One possibility: two routes add strength



0 0.05 0.1 0.15 0.2 0.25

SLEEP

RESTTIRED

BEDWAKE

AWAKENAP

DREAMYAWNDROWSYBLANKETSNORESLUMBERDOZEPEACE

SLEEPNIGHT

HOURSMORNING

ASLEEPSLEEPYAWAKENEDSPENT

STUDYLIST

EXTRALIST

(top 8)

Mini-course on Artificial Neural Networks and Bayesian Networks Michal Rosen-Zvi Mini-course on ANN...

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