1.
Machine Learning & Deep Learning
Are Fun
Toan Dang

2.
Toan Dang Anh
Technical Architect – NashTech Global
• More than 15 years of experience in providing
Database solutions for big corporations.
• Rich experience using various solutions with real
time processing, high availability and scale out.
• Providing strategic solutions to various project about
Database, Data Warehouse, BI, Big Data and
Machine Learning & Deep Learning.
• Email: datoan76@gmail.com
• Skype: toan.dang

3.
TABLE OF AGENDA
Understanding Machine Learning
Machine Learning Algorithms & Application
Demo Machine Learning
Why Deep Learning?
Deep Learning Algorithms & Application
Demo Deep Learning

4.
1. Understanding Machine Learning
What is Machine Learning?

5.
Understanding Machine Learning
An application of artificial
intelligence (AI)
Learn and improve from
experience
Access data & use it learn for
themselves
Look for patterns in data and
make better decisions

6.
Artificial Intelligence History
Dartmouth Assistant
Professor John
McCarthy
1956
1980
2010
2015
Machine Learning:
offloading optimization
Deep Learning
Feature Learning
Very Deep Learning
Very Deep Networks with
Skip Connections

7.
Features
Features are the variables found in the given problem set that can
strongly/sufficiently help us build an accurate predictive model.

8.
Features
Weight(g) Wingspan(cm) Webbed feet? Back color Species
1 1000.1 125.0 No Brown Buteo jamaicensis
2 3000.7 200 No Gray Sagittarius
3 3300.0 220.3 No Gray Sagittarius
4 4100.0 136.0 Yes Black Gavia immer
5 3.0 11.0 No Green Calothorax lucifer
6 570.0 75.0 No Black Campephilus Principalis
1. Weight
2. Wingspan
3. Webbed feet
4. Back color
Feature Name Feature Value
1. Numeric
2. Binary
3. Enumeration
(color)

9.
Training Set
A training set is the set of training examples we’ll use to train our
machine learning algorithms
Weight(g) Wingspan(cm) Webbed feet? Back color Species
1 1000.1 125.0 No Brown Buteo jamaicensis
2 3000.7 200 No Gray Sagittarius
3 3300.0 220.3 No Gray Sagittarius
4 4100.0 136.0 Yes Black Gavia immer
5 3.0 11.0 No Green Calothorax lucifer
6 570.0 75.0 No Black Campephilus Principalis

10.
Label - Target Variable
o In classification the target variable takes on a nominal value
o In the task of regression its value could be continuous
o In a training set the target variable is known
Weight(g) Wingspan(cm) Webbed feet? Back color Species
1 1000.1 125.0 No Brown Buteo jamaicensis
2 3000.7 200 No Gray Sagittarius
3 3300.0 220.3 No Gray Sagittarius
4 4100.0 136.0 Yes Black Gavia immer
5 3.0 11.0 No Green Calothorax lucifer
6 570.0 75.0 No Black Campephilus Principalis

11.
Test Set
Can split from Training set
To test machine
learning algorithms
Must be separated
with training set

12.
Responsibility of MACHINE LEARNING
In the classification problem the target variables are called classes
Features Label

13.
SIMPLE WORKFLOW
Feature
Extraction
Label
New/Test
Data
Training Model
Model
Label
Raw Data
(Train)

14.
2. Classification Algorithms & Application
Some algorithms & Steps to build application

15.
Unsupervised Learning
Clustering Analysis
K-Means Clustering
Hierarchical Clustering
Dimension Reduction
Decision Tree
K-Nearest Neighbors
Supervised Learning
Regression
Linear Regression
Logistic Regression
Polynomial Regression
Neural Networks
Classification
Decision Tree
K-Nearest Neighbors
Support vector machine
Logistic Regression
Naive Bayes
Random Forests

16.
Predict
Nominal Value
How to choose the right algorithm
Predict
or
Groups
Unsupervised
Learning
Groups
what’s
your
target
value
Classification
Continuous Value
Regression
Supervised
Learning

17.
Steps To Develop Machine Learning Applications

18.
Steps To Develop Machine Learning Apps
Define Object Data Collect
Data
Preparation
Modeling
Evaluation
Deployment
Monitor
Operate

19.
Process To Develop Application
1
What do we want to find out?
Classify, predict or group
Define Object
2
3
5
6
7
Evaluate how well the algorithm learned from
its experience
Evaluate Accuracy, lost error, validate errors
Evaluate Model
Data need to be gathered in an electronic
format suitable for analysis. ETL, API, Web
Scraping
Data Collect
Deploy model to API and use
Deploy & Use
Need features in a special format
Need them to be integers
Analyze data by Plotting
Data Preparation
Monitoring the model & the accuracy. Re-
train model when need or data change.
Improving model performance
Monitor/Operate
4
Split Train & Test set.
Feature Selection & Feature Engineering
Training Model using right Algorithms.
Integrate multi models, tuning parameters
Model

20.
Machine Learning Examples

21.
Linear Regrestion

22.
Logistic Regresion

23.
Decision Tree
Clinical Decision Tree - Weather Decision Tree
Chatbot Decision Tree

24.
SVM
Face detect, Handwrite recognition
Classification images

25.
Association Rule
Market Basket Analysis

26.
3. Demo Machine Learning
Movie Recommendation

27.
Collaborative Filtering
Recommendation with User-Based & Item-Based
Content-Based using TF-IDF (Terms-Frequency -Inverse Document Frequency)

28.
Movie Recommendation Architecture
1.User access to website
RDBMS
4.Build Model
Collaborative
filtering
2.Update DB
3.Send to Hadoop
Recommendation
(API)
8.Recommend
Movies
118268, rating:5.2997
66389, rating:5.2092
173275, rating:5.1517
144202, rating:5.1516
117352, rating:5.1411
94101, rating:5.09185
…..
Ratings Model
10020 =>118268:5.2997
10020 => 66389:5.2092
10020 => 173275:5.15176.Deploy

29.
Movie Recommendation.
• User watch a movie on the website,
which movie may be you like?
System Recommendation

30.
4. DEEP LEARNING

31.
Neural Network

32.
Neural Network
Face detection, Recognition, Translation, Object Detection
Tracking moving object, Text classification, voice recognition …
http://www.asimovinstitute.org/neural-network-zoo/

33.
Recall Fully Connected Neural Network
Total number of connections in this network?

34.
Recall Fully Connected Neural Network
Total number of connections in this network?
= 50x50x3x2,000 + 2,000x2
15,004,000

35.
Neural Network Problem
1. What exactly is deep learning ?
2. Why is it generally better than other methods on
image, speech and certain other types of data?
The short answers:
‘Deep Learning’ means using a neural network
with several layers of nodes between input and output
The series of layers between input & output do
feature identification and processing in a series of stages,
just as our brains seem to.

36.
Difference Machine Learning & Deep Learning
https://towardsdatascience.com/why-deep-learning-is-needed-over-traditional-machine-learning-1b6a99177063

37.
AI vs ML vs DL
https://goo.gl/6fKtGY

38.
Deep Learning Comes In
https://github.com/tavgreen/cnn-and-dnn

39.
Deep Learning
Was the first year that neural nets grew to prominence
Alex Krizhevsky used them to win that year’s ImageNet
Competition 2012
Dropping the classification error record from 26% to 15%
2012

40.
Deep Learning
Deep Learning Definition
Methods based on learning data representations,
as opposed to task-specific algorithms.
Generally based on Artificial Neural Network

41.
Deep Learning
Feature learning - Representation learning In Machine Learning
Set of techniques that allows a system to automatically
discover the representations needed for feature detection
or Classification from raw data
This replaces manual feature engineering and allows a
machine to both learn the features and use them to perform a
specific task

42.
Deep Learning
Example Of Difference Representations
https://towardsdatascience.com/deep-learning-d5fe55326e57

43.
Deep Learning
Learning data representations
https://towardsdatascience.com/deep-learning-d5fe55326e57

44.
5. Deep Learning Algorithms & Application
https://goo.gl/sD77JS

45.
Convolutional Neural Network
CNN - Almost Deep learning base on
https://goo.gl/jHm3Hj

46.
Convolutional Neural Network
Convolution +
ReLU
Strides + Padding Pooling Layer Fully Connected
Preserves the relationship
between pixels by
learning image features
using small squares
Calculated by input image
matrix and a filter or
kernel.
Stride: number of pixels
shifts over the input
matrix
Padding: filter does not fit
perfectly fit the input
image (drop or add zero)
Reduce the number of
parameters (Reduce
dimension -
Subsampling).
Flattened our matrix into
vector
Apply activation function
to classify output

47.
Convolutional - Filters
CNN - Almost Deep learning base on
https://goo.gl/jHm3Hj

48.
Convolutional Neural Network
Example: ConvNetJS

49.
Convolutional Neural Network
Autoencoders & Decoders, R-CNN, Fast-RCNN, Faster-RCNN, Yolov1-3,
SSD

50.
Convolutional Neural Network
Autoencoders (AE) & Decoder
https://goo.gl/CBk86W

51.
Convolutional Neural Network
Object detection and segmentation
R-CNN, Fast-RCNN, Faster-RCNN, Yolov1-3, SSD
https://goo.gl/SuuQ46

52.
Recurrent Neural Networks - RNN
For sequential data and among others used by Apples Siri and Googles
Voice Search

53.
Recurrent Neural Networks
Sequence Recurrent Memory Parameters
Use of sequential
information
Perform the same
task for every
element of
a sequence
Limit to looking back
only a few steps
RNN shares the
same parameters
(U,V,W)

54.
Two Problems Of RNN
Exploding Gradients
Assigned higher weights
(gradients):
The steeper the slope and
the faster a model can learn
 Low accuracy
Vanishing Gradients
The gradient are too small:
 The model stops learning
 or takes way too long.
Solved by LSTM

55.
Recurrent Neural Networks - LSTM
Improvement Of RNN

56.
LSTMs reduce vanishing gradient problem
- The darker the shade, the greater the sensitivity
- The sensitivity decays exponentially over time as new inputs overwrite the
activation of hidden unit and the network ‘forgets’ the first input
Standard Recurrent Network LSTM Network

57.
Reinforcement Learning
How software agents ought to take actions in an environment so as to
maximize some notion of cumulative reward

58.
Reinforcement Learning

59.
Reinforcement Learning
Q-learning update
Alpha (0<α≤1) is the extent to which our Q-values are being updated
in every iteration
Gamma: (0≤γ≤1): determines how much importance we want to give
to future rewards.
β: determines the sensitivity of the choice probabilities to difference
in values to calculate Probability (Soft max)

60.
Reinforcement Learning
Value functions & policy gradients

61.
Reinforcement Learning
Deep Q-Network (DQN) vs Human

62.
Reinforcement Learning
Alphago: first release October 2015. At the 2017 Future of Go Summit, AlphaGo
beat Ke Jie, the world No.1 ranked player at the time, in a three-game match

63.
Frameworks

64.
6. Demo Deep Learning
Image Captioning

65.
Image Captioning Architecture

66.
Image Captioning Architecture

67.
Image Captioning Process
Load dataset in batch and
transform to PyTorch tensor
224x224x3. Batchsize=10
COCO dataset
Process caption data and build
vocabulary (words and count)
Pre-processing
Caption
Load pretrained model Resnet50
Extract the features
Connected to Decoder Layer
CNN Encoder
Do language modelling up to the word level
Hidden state size 512, embedded size: 512
Output: model's predicted score
RNN Decoder
Using flask to expose API
Develop Application to
consume API
Re-train model
Expose API
Load model & Vocabulary
Transform input image
Test sampler
Generate prediction
Prediction
Apply BLEU to calculate
model’s score
Apply BEAM Search to
calculate probability of words
Generate sentence
Validation
Use pre-trained ResNet model (transfer learning)
RNN with Embedding layer, a LSTM layer and a
fully-connected layer
Epochs: 20, CUDA, batchsize 32
Minimum word count: 5
Train Model

68.
Demo: Image Captioning

69.
THANK YOU
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