Deconstructing Deep Learning - Big Data Conference Europe 2019 · Deep Learning 101 Convolutional Neural Networks Recurrent Neural Networks @markawest. How computers «see» images

Deconstructing Deep Learning

@markawest

A Practical-ish Introduction to Data Science, Part 2

Who Am I?

@markawest

Who Am I?

• Java Developer and Architect.

@markawest

Who Am I?


• Currently managing a team of Data Scientists at Bouvet Oslo.

@markawest

Who Am I?


• Currently managing a team of Data Scientists at Bouvet Oslo.

• Leader javaBin (Norwegian JUG).

@markawest

Agenda

Deep Learning 101

Convolutional Neural

Networks

Recurrent Neural

Networks

@markawest

Agenda

Deep Learning 101


Networks

Recurrent Neural

Networks

@markawest

Agenda

Deep Learning 101


Networks

Recurrent Neural

Networks

@markawest

Agenda

Deep Learning 101


Networks

Recurrent Neural

Networks

@markawest

Deep Learning 101

Deep Learning 101 Convolutional Neural Networks

Recurrent Neural Networks

@markawest

@markawest

Deep LearningBiologically inspired machine learning with multi-layered

Artificial Neural Networks.

Source: Neuroscape Neuroscience Center, USCF Source: becominghuman.ai/@venkateshtata9

«Vanilla» Artificial Neural Network

•Multi Layer Peceptron.• Feed-forward.• Densely Connected.•Weighted connections.

@markawest

@markawest

Artificial Neural Network Structure

INPUT LAYEREntry point for incoming

data.

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data.

@markawest


HIDDEN LAYERIdentify features from input data and correlate these to

the correct output.


data.

HIDDEN LAYERIdentify features from input data and correlate these to

the correct output.

OUTPUT LAYERDeliver the end result

from the ANN

@markawest


INPUT LAYER

MULTIPLE HIDDEN LAYERS OUTPUT LAYER

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Deep Artificial Neural Network

i0

i1

NODEINPUTS WEIGHTS

o0

OUTPUT

Artificial Neural Network Node

@markawest

w1

w0

Sum ofWeighted

Inputs

ActivationFunction

i0

i1

NODEINPUTS WEIGHTS

o0

OUTPUT


@markawest

w1

w0

Sum ofWeighted

Inputs

ActivationFunction

It is not unusual for a single node to receive inputs numbering in the hundreds of thousands.

This is especially true for densely connected ANNs, whereeach node recieves inputs from all nodes in the previous

layer.

i0

i1

NODEINPUTS WEIGHTS

o0

OUTPUT


@markawest

w1

w0

Sum ofWeighted

Inputs

ActivationFunction

Each input is paired with an adjustable weight. Weights act to amplify or dampen

specific inputs.

Weights are adjusted during the training phase to improve accuracy of the ANN.

i0

i1

NODEINPUTS WEIGHTS

o0

OUTPUT


@markawest

w1

w0

Weighted Inputs aresummed up and then

passed through an Activation Function.

The Activation Functiondecides what strength ofsignal (if any) should be

sent onwards.

Sum ofWeighted

Inputs

ActivationFunction

i0

i1

NODEINPUTS WEIGHTS

o0

OUTPUT


@markawest

w1

w0

Sum ofWeighted

Inputs

ActivationFunction

The result from the Activation Function is sent onwards to the next layer in the ANN.

In a densely connected ANN the output from this node would be sent to all nodes in the next layer.

i0

i1

NODEINPUTS WEIGHTS

o0

OUTPUT


@markawest

w1

w0

Sum ofWeighted

Inputs

ActivationFunction

Activation Functions

@markawest

0

0-1

-1

1

1

SigmoidTanhReLU

Out

put

Input

• Convert input signals intooutput signals via simple calculations.

• Many different types withdifferent behaviour and different pros and cons.

Training an Artificial Neural Network

@markawest


@markawest

«NOT A COW»

0.15

1. Generate PredictionTraining Data sent through theANN to generate predictiions


@markawest

«NOT A COW»

0.15PREDICTED: NOT A COW (0.15)EXPECTED: COW (1.00)

2. Calculate LossA «Loss Function» calculates differencebetween Predicted and Expected values



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«NOT A COW»

0.15PREDICTED: NOT A COW (0.15)EXPECTED: COW (1.00)

3. Update WeightsWeights updated via «back-propagation» to reduce loss

2. Calculate LossA «Loss Function» calculates differencebetween Predicted and Expected values


Updating Weights via Gradient Descent

@markawest

GLOBAL MINIMUM

LOCAL MINIMUM

LOSS

WEIGHT

• Goal: Increment Weightvalues to find those that givethe lowest Loss values.

• Challenges: • No map.• Avoiding Local Minima.• Tuning the Learning Rate.

START

Deep Learning 101 Summary• An Artificial Neural Network (ANN) is a set of interconnected Nodes.

• Nodes are organised into Layers (Input, Hidden and Output).

• Deep Learning refers to ANNs with multiple Hidden Layers.

• Inputs to Nodes are amplified or dampened by Weights.

• Weights are tuned via Gradient Descent to optimise the ANNs predictions.

@markawest

Convolutional Neural Networks



@markawest

How computers «see» images

@markawest

0.59 1.0 0.9 0.87

0.25 0.29 0.42 0.46

0.94 0.76 0.89 0.96

0.86 0.93 0.88 0.85

@markawest

Greyscale (28 x 28 x 1) RGB/Colour (28 x 28 x 3)

28 p

ixel

s

28 p

ixel

s

28 pixels3 Channels

Extra «channels» increase complexity

28 pixels1 Channel

Example «Vanilla» ANN Configuration

@markawest

640 x 640 x 3

WEIGHTED CONNECTIONS

1 287 782 400

INPUT LAYER

1 228 800 Nodes

HIDDEN LAYER

1 048 Nodes

Another Approach: Convolutional Filters

@markawest

-1 0 +1

-2 0 +2

-1 0 +1

+1 +2 +1

0 0 0

-1 -2 -1

VerticalEdge

Detection

HorizontalEdge

Detection

• Detect and amplify specific features in images.

• Reusable across images.

• Are weights that are learned and optimised via back-propagation during training.

• A CNN will have many convolutional filters, where eachone learns to detect a specific feature.

Applying Convolutional Filters

@markawest

0 0 0 0 0 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 0 0 0 0 0

Original ImageConv .Filter Feature Map

-1 0 +1-2 0 +2-1 0 +1

3 x 3 Pixels1 x 1 Stride

+ =


@markawest

0 0 0 0 0 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 0 0 0 0 0

3

Original Image Feature Map

-1 0 +1-2 0 +2-1 0 +1

+ =-1 0 +1

-2 0 +2

-1 0 +1

Conv .Filter



@markawest

0 0 0 0 0 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 0 0 0 0 0

3 0


-1 0 +1-2 0 +2-1 0 +1

+ =-1 0 +1

-2 0 +2

-1 0 +1

Conv .Filter



@markawest

0 0 0 0 0 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 0 0 0 0 0

3 0 0


-1 0 +1-2 0 +2-1 0 +1

+ =-1 0 +1

-2 0 +2

-1 0 +1

Conv .Filter



@markawest

0 0 0 0 0 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 0 0 0 0 0

3 0 0 -3


-1 0 +1-2 0 +2-1 0 +1

+ =-1 0 +1

-2 0 +2

-1 0 +1

Conv .Filter



@markawest

0 0 0 0 0 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 0 0 0 0 0

3 0 0 -34


-1 0 +1-2 0 +2-1 0 +1

+ =

-1 0 +1

-2 0 +2

-1 0 +1

Conv .Filter



@markawest

0 0 0 0 0 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 0 0 0 0 0

3 0 0 -34 0 0 -44 0 0 -43 0 0 -4

Original Image Feature Map+ =

-1 0 +1

-2 0 +2

-1 0 +1

-1 0 +1-2 0 +2-1 0 +1

Conv .Filter


Applying ReLU Activation Function

@markawest

3 0 0 04 0 0 04 0 0 03 0 0 0

ReLUFeature Map New Feature Map+ =3 0 0 -34 0 0 -44 0 0 -43 0 0 -4

Out

put

Input

0

0

10

10-10

-10

Dimensionality Reduction via Pooling

@markawest

3 0 0 04 0 0 04 0 0 03 0 0 0

Feature MapPool


+ =

Option 1: Max

Pooling

Option 2: AveragePooling

Option 3: Sum

Pooling

New Feature Maps


@markawest

3 0 0 04 0 0 04 0 0 03 0 0 0

Feature MapPool + =4 1.75 7

New Feature Maps


Option 1: Max

Pooling


Option 3: Sum

Pooling

3 0 0 04 0 0 04 0 0 03 0 0 0


@markawest

Feature MapPool + =4 0 1.75 0 7 0

New Feature Maps


Option 1: Max

Pooling


Option 3: Sum

Pooling


@markawest

3 0 0 04 0 0 04 0 0 03 0 0 0

Feature MapPool + =4 04

1.75 0

1.75

7 07

New Feature Maps


Option 1: Max

Pooling


Option 3: Sum

Pooling

3 0 0 04 0 0 04 0 0 03 0 0 0


@markawest

Feature MapPool + =4 04 0

1.75 0

1.75 0

7 07 0

New Feature Maps


Option 1: Max

Pooling


Option 3: Sum

Pooling

Feature Identification and Dimensionality Reduction

@markawest

4 04 0

3 0 0 -34 0 0 -44 0 0 -43 0 0 -4

0 0 0 0 0 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 1 1 1 1 00 0 0 0 0 0

3 0 0 04 0 0 04 0 0 03 0 0 0

Original Image

1. Post Conv Filter 2. Post ReLU 3. Post Max Pooling

Feature Map Processing

Use Case: Fashion MNIST

@markawest

• 70 000 labelled images of clothing.

• Each image : 28 x 28 greyscale pixels.

• Distributed evenly across 10 classes.

• Goal: Train a CNN that can identifywhich class a given image belongs to.

CNN Implementation

@markawest

https://github.com/markwest1972/CNN-Example-Google-Colaboratory


@markawest

HIDDEN OUTPUTINPUT

INPUT DENSECONV MAXPOOLING DROPOUT FLATTEN DENSE

Learn a set ofConvolutional Filters

that generateFeature Maps for each Input Image

Randomlydeactivate

nodes during training to fight

overfitting

Convert data from 3D to 1D for

processing by Dense Layers

Entry point forInput Image (28 x 28 x 1)

Report probabilities

for the 10 target Classes

CNN Architecture Overview

DimensionalityReduction of Feature

Maps

CorrelateFeature Maps to

a given Class

CONV

Applies 32 Convolutional Filters to

the Input Image

Resulting 3D array, containing 32 Feature Maps


28 x 28 x 1 array of

Pixel Values

@markawest

@markawest50% reduction in size of

Feature Maps via Max PoolingM

AXPO

OLI

NG

32 Feature Maps of26 x 26 pixels

32 Feature Maps of13 x 13 Pixels


Training and Testing the CNN

• Train the CNN with Training Data, updatingweights via Back Propagation every 256 records.

• Repeat 10 times (also known as epochs).

• Test the CNN with Test Data and score based on accuracy (match between actualclass vs. predicted class).

@markawest

Fashion MNIST Dataset (70 000)

Training Data (60 000)

Test Data (10 000)

CNN Test Results

@markawest

• Precision: Percent ofcorrect class identifications.

• Recall: Percent of correctlyidentified class members.

• F1-score: Harmonic meanof recall and precision.

• Accuracy: Percent ofcorrect predictions.

Some examples of incorrectly classified images

@markawest

CNN Summary

• CNNs are spatially aware and are therefore particularly efficient for image processing.

• CNNs use learnable and reusable Convolutional Filters to identify features in images.

• CNNs require Dense Layers to map features to classes.

• Pooling is an optional step to reduce the dimensionality of input data.

• Understanding the internal representations of ANNs can give insight into howthey can be improved.

@markawest




@markawest

Which way is the arrow moving?

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Did this person enjoy the movie?

«If you have never read the book you might like this movie. If you have read the book (as I have) then you will hate it.»

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A question of time!

«Vanilla» ANNs (such as MLPs) are unable to handle

sequential input!

@markawest

INPUT

HIDDEN

OUTPUT

Recurrent Neural Networks to the Rescue!

@markawest

Input Layer cycles through sequential inputs (i.e. words in a sentence) in given order

Hidden Layer state is maintained betweensequential inputs via a loop, also known as the

RNN’s «working» memory

Output Layer gives final prediction after all sequential inputs have been processed

Unrolling a Recurrent Neural Network

@markawest

i[1-4]

o4

INPUT

HIDDEN

OUTPUT

Unrolling a Recurrent Neural Network

@markawest

=

i[1] i[2] i[3] i[4]

o1 o2 o3 o4

i[1-4]

o4

INPUT

HIDDEN

OUTPUT

Identical ANNs sharing weights and hidden state

The Limitations of «Short-Term» Memory

@markawest

INFLUENCE ON RNN OUTPUT

“the trailers were the best part of the whole movie.”

trailersthe were the best part of the whole movie

Long Short-Term Memory (LSTM) Networks

@markawest

=

i[1] i[2] i[3] i[4]

o1 o2 o3 o4

i[1-4]

o4

«Short Term

Memory»

«Long Term

Memory»

Identical ANNs sharing weights and hidden state

INPUTGATE

OUTPUTGATE

FORGETGATE

concat

OUTPUT (n)

SHORT TERM (n)

LONG TERM (n)

SHORT TERM (n-1)

LONG TERM (n-1)FORGET

GATE

concat

OUTPUT (n-1)

OUTPUTGATE

INPUT (n) INPUT (n+1)

A LSTM Node

@markawest

Use Case : IMDB Reviews

• 50 000 IMDB Reviews, each one classifiedas a "positive" or "negative" review.

• Reviews encoded as a sequence ofintegers, with all punctuation removed.

• Integer represents that words overall frequency in the dataset.

• Goal: Train an LSTM that can accuratelyclassify a movie review.

IMDB Reviews Dataset (50 000)

Training Data(25 000)

Positive (12 500)

Negative (12 500)

Test Data(25 000)

Positive (12 500)

Negative (12 500)

@markawest

Example Positive Review

ENCODED

1, 13, 296, 4, 20, 11, 6, 4435, 5, 13, 66, 447, 12, 4, 177, 9, 321, 5, 4,

114, 9, 518, 427, 642, 160, 2468, 7, 4, 20, 9, 407, 4, 228, 63, 2363, 80, 30, 626, 515, 13, 386, 12, 8, 316, 37, 1232, 4, 698, 1285, 5,

262, 8, 32, 5247, 140, 5, 67, 45, 87

DECODED

<START> i watched the movie in a preview and i really loved it the cast

is excellent and the plot is sometimesabsolutely hilarious another highlight

of the movie is definitely the musicwhich hopefully will be released sooni recommend it to everyone who likes the british humour and especially to

all musicians go and see it's great

@markawest

Pre-Processing Dataset

• Reduced datasetvocabulary to the most popular10 000 words.

• Padded / Truncated eachreview so that all are 500 wordslong.

@markawest

<PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <START> i saw this at the <UNKNOWN> film festival it was awful every clichéd violent richboy fantasy was on display you just knew how it was going to end especially with all the shots of the<UNKNOWN> wife and the rape of the first girl br br the worst part was the q a with the directorwriter and writer producer they tried to come across as <UNKNOWN> but you could tell they're the types that get off on violence i bet anything they frequent <UNKNOWN> and do drugs br br don't waste yourtime i had to keep my boyfriend from walking out of it

Word Embedding for NLP

• Plots words in a multi-dimensional space.

• Different dimensions capture different linguistic relationships between words.

• The closer the words, the stronger therelationship.

• Pre-trained models available (GloVe, fastText, Word2Vec).

@markawestSource: www.shanelynn.ie

LSTM Implementation

@markawest

https://github.com/markwest1972/LSTM-Example-Google-Colaboratory


LSTM Architecture Overview

HIDDEN OUTPUTINPUT

INPUT DENSEEMBEDDING DROPOUT LSTM DROPOUT

@markawest

Accept a list of 500 integers, each

representing a wordof a truncated / padded movie

review

Randomly deactivatenodes during training to

fight overfitting

Randomly deactivatenodes during training to

fight overfitting

Use a Sigmoid ActivationFunction to return a value

between 0 (negative) and 1 (positive)

Learns a 32 dimensionalWord Embedding for all words in the training set

Learn to differentiatebetween positive and

negative reviews based onword order and word

meaning

@markawest


Learn a 32 dimensional Word Embedding for all

10 000 uniquewords in thetraining set.

1 2 3 4 5 ... 321 -0.1 1.1 7.3 2.3 4.5 ... 4.92 1.0 9.8 2.2 -4.3 6.7 ... 8.73 9.6 7.7 -6.9 1.3 0.3 ... -0.9... ... ... ... ... ... ... ...

10 000 0 -1.1 3.3 9.9 6 ... -1.0

Dimensions

Wor

dsTable Coordinates updated via Back Propagation

@markawest


Learn to differentiate betweenpositive and negative reviews

based on word order and word meaning 1 2 3 4 5 ... 32

1 -0.1 1.1 7.3 2.3 4.5 ... 4.9

2 1.0 9.8 2.2 -4.3 6.7 ... 8.7

3 9.6 7.7 -6.9 1.3 0.3 ... -0.9

... ... ... ... ... ... ... ...

10 000 0 -1.1 3.3 9.9 6 ... -1.0

Dimensions

Wor

ds

1, 13, 296, 4, 20, 11, 6, 4435, 5, 13, 66, 447, 12, 4, 177, 9, 321, 5, 4, 114, 9, 518, 427, 642, 160, 2468, 7, 4, 20, 9, 407, 4, 228, 63, 2363, 80, 30, 626, 515, 13, 386, 12, 8, 316, 37, 1232, 4, 698,

1285, 5, 262, 8, 32, 5247, 140, 5, 67, 45, 87

Training and Testing the LSTM

• Train the LSTM with Training Data, updating weights via Back Propagationevery 256 records.

• Repeat 3 times (also known as epochs).

• Test the LSTM with Test Data and score based on accuracy (match betweenactual class vs. predicted class).

@markawest

IMDB Reviews Dataset (50 000)

Training Data(25 000)

Positive (12 500)

Negative (12 500)

Test Data(25 000)

Positive (12 500)

Negative (12 500)

LSTM Test Results

@markawest

• Precision: Percent ofcorrect class identifications.

• Recall: Percent of correctlyidentified class members.

• F1-score: Harmonic meanof recall and precision.

• Accuracy: Percent ofcorrect predictions.

Example of an incorrectly classified review

«<START> i was very disappointed when this show was canceledalthough i can not vote i live on the island of i sat down to see the show

on <UNKNOWN> and was very surprised that it didn't aired the nextday i read on the internet that it was canceled br br it's true not everyone was as much talented as the other but there were very talented

people singing br br i find it very sad for them br br that they worked so hard and there dreams came <UNKNOWN> down br br its a pity br br»

@markawest

Predicted: Negative, Actual: Positive





@markawest






@markawest






@markawest


RNN/LSTM Summary• RNNs handle sequential data by persisting the hidden state via short-term or

«working» memory.

• LSTMs extend RNNs by adding long-term memory.

• Word Embedding is a powerful technique for modelling semantic relationships between words.

• Pre-processing of data is vital for securing good results.

• Precision, Recall, F1-score and Accuracy are different metrics for measuringmodel performance.

@markawest

Further Reading (Blog articles and code)

BLOG SERIES1. https://www.bouvet.no/bouvet-deler/an-

introduction-to-deep-learning

2. https://www.bouvet.no/bouvet-deler/understanding-convolutional-neural-networks-part-1

3. https://www.bouvet.no/bouvet-deler/understanding-convolutional-neural-networks-part-2

4. https://www.bouvet.no/bouvet-deler/explaining-recurrent-neural-networks

CNN & RNN IMPLEMENTATIONS• https://github.com/markwest1972/CNN-

Example-Google-Colaboratory

• https://github.com/markwest1972/LSTM-Example-Google-Colaboratory

@markawest

https://www.bouvet.no/bouvet-deler/an-introduction-to-deep-learning

https://www.bouvet.no/bouvet-deler/understanding-convolutional-neural-networks-part-1

https://www.bouvet.no/bouvet-deler/understanding-convolutional-neural-networks-part-2

https://www.bouvet.no/bouvet-deler/explaining-recurrent-neural-networks



Thanks for listening!

@markawest

Documents

Deconstructing Deep Learning - Big Data Conference Europe 2019 · Deep Learning 101 Convolutional Neural Networks Recurrent Neural Networks @markawest. How computers «see» images