machine learning con matlab

1. 1
© 2019 The MathWorks, Inc.
Machine Learning
and Deep Learning with MATLAB
Sergio E. Obando Quintero, Ph.D.
Application Engineer
Source: https://xkcd.com/1838/

2. 2
Machine learning in action
CamVid Dataset
1. Segmentation and Recognition Using Structure from Motion Point Clouds, ECCV 2008
2. Semantic Object Classes in Video: A High-Definition Ground Truth Database, Pattern Recognition Letters

3. 3
Machine Learning is Everywhere
Automobile Industrial Automation CES & Aero Defense Energy & Finance
Tire Wear
Detect
Oversteer
Engine Health
(Pred Maintenance)
Forecasting &
Risk Analysis
Overlay metrology
improvement
Building energy
use optimization
Portfolio
Allocation
Telecom customer
churn prediction

4. 4
Artificial Intelligence
Machine Learning
AI, Machine Learning and Deep Learning
Deep Learning
Timeline
1950s Today
1980s
Application
Breadth
Automated Driving
Speech Recognition
Robotics
Object Recognition
Bioinformatics
Recommender Systems
Spam Detection
Fraud Detection
Weather Forecasting
Algorithmic Trading
Sentiment Analysis
Medical Diagnosis
Health Monitoring
Computer Board Games
Machine Translation
Knowledge Representation
Perception
Reasoning
Interactive Programs
Expert Systems

5. 5
What is Machine Learning?
learn complex non-
linear relationships
Solution is too complex for hand written rules or equations
update as more data
becomes available
Solution needs to adapt with changing data
learn efficiently from
very large data sets
Solution needs to scale
Speech Recognition Object Recognition Engine Health Monitoring
Weather Forecasting Energy Load Forecasting Stock Market Prediction
IoT Analytics Taxi Availability Airline Flight Delays
Ability to learn from data without being explicitly programmed

6. 6
Real-life saving pattern-detection application!
Read more...
From
Traditional Monitoring
▪ Outdated technology
▪ Not suitable for children
▪ Considerable patient effort
To
Respiratory Digital Health
▪ Sensors, apps, etc. to monitor and
control symptoms
▪ Convenient effortless
▪ Objective symptom measurement

7. 7
Goals Part I
▪ Overview of machine learning
▪ Machine learning models & techniques available in MATLAB
▪ Using MATLAB to streamline your machine learning workflow

8. 8
Machine Learning
Machine learning uses data and produces a program to perform a task
Traditional Approach Machine Learning Approach
𝑚𝑜𝑑𝑒𝑙 = <
𝑴𝒂𝒄𝒉𝒊𝒏𝒆
𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈
𝑨𝒍𝒈𝒐𝒓𝒊𝒕𝒉𝒎
>(𝑠𝑒𝑛𝑠𝑜𝑟_𝑑𝑎𝑡𝑎, 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦)
Computer
Program
Machine
Learning
𝑚𝑜𝑑𝑒𝑙: Inputs → Outputs
Hand Written Program Formula or Equation
If X_acc > 0.5
then “SITTING”
If Y_acc < 4 and Z_acc > 5
then “STANDING”
…
𝑌𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦
= 𝛽1𝑋𝑎𝑐𝑐 + 𝛽2𝑌𝑎𝑐𝑐
+ 𝛽3𝑍𝑎𝑐𝑐 +
…
Task: Human Activity Detection

9. 9
Steps Challenge
Access and Explore data
Data diversity
Numeric, Images, Signals, Text – not always tabular
Preprocess Data
Lack of domain tools
Filtering and feature extraction
Feature selection and transformation
Develop Predictive Models
Time consuming
Train and compare several models to find the “best”
Select optimal parameters and avoid overfitting
Integrate Analytics with Systems
Platform diversity
Translate analytics to production
Deploy on different target platforms
Iterate
Challenges in Machine Learning

10. 10
Different Types of Machine Learning
Machine
Learning
Supervised
Learning
Classification
Regression
Unsupervised
Learning
Clustering
Group and interpret
data based only
on input data
Develop predictive
model based on both
input and output data
Type of Learning Categories of Algorithms
• Discover a good internal representation
• Learn a low dimensional representation
• Output is a real number (temperature,
stock prices).
• Output is a choice between classes
• (True, False) (Red, Blue, Green)

11. 11
Different Types of Machine Learning
Machine
Learning
Supervised
Learning
Classification
Regression
Unsupervised
Learning
Clustering
Group and interpret
data based only
on input data
Develop predictive
model based on both
input and output data
Type of Learning Categories of Algorithms
• k-means, k-medoids, Hierarchical, Gaussian
Mixture Models, Nearest Neighbors, Hidden
Markov Models
• Linear Regression, Generalized Linear Regression,
Nonlinear Regression, Mixed-Effects, Stepwise,
Gaussian Process Regression
• Decision Trees, Nearest Neighbor, Support
Vector Machines, Ensemble Methods,
Discriminant Analysis

12. 12
Machine Learning Workflow
Integrate Analytics with
Systems
Desktop Apps
Enterprise Scale
Systems
Embedded Devices
and Hardware
Files
Databases
Sensors
Access and Explore
Data
Develop Predictive
Models
Model Creation e.g.
Machine Learning
Model
Validation
Parameter
Optimization
Preprocess Data
Working with
Messy Data
Data Reduction/
Transformation
Feature
Extraction

13. 13
MODEL
PREDICTION
Machine Learning Workflow
Train: Iterate till you find the best model
Predict: Integrate trained models into applications
MODEL
SUPERVISED
LEARNING
CLASSIFICATION
REGRESSION
PREPROCESS
DATA
SUMMARY
STATISTICS
PCA
FILTERS
CLUSTER
ANALYSIS
LOAD
DATA
PREPROCESS
DATA
SUMMARY
STATISTICS
PCA
FILTERS
CLUSTER
ANALYSIS
NEW
DATA

14. 14
Classification: Human Activity Recognition
Objective: Train a classifier to classify
human activity from sensor data
Data:
Approach:
– Extract features from raw sensor signals
– Train and compare classifiers
– Test results on new sensor data
Predictors 3-axial Accelerometer and
Gyroscope data
Response Activity:

15. 15
PREDICTION
MODEL
Machine Learning Workflow for Classification Example
Train: Iterate till you find the best model
Predict: Integrate trained models into applications
MODEL
SUPERVISED
LEARNING
CLASSIFICATION
REGRESSION
PREPROCESS
DATA
SUMMARY
STATISTICS
PCA
FILTERS
CLUSTER
ANALYSIS
LOAD
DATA
PREPROCESS
DATA
SUMMARY
STATISTICS
PCA
FILTERS
CLUSTER
ANALYSIS
TEST
DATA
1. Mean
2. Standard
deviation
3. PCA
Classification
Learner
1. Mean
2. Standard
deviation
3. PCA

16. 17
Training: Machine Learning with MATLAB
After this course you will be able to:
▪ Discover natural patterns in data
▪ Create predictive models
▪ Validate the predictions of a model
▪ Simplify and improve models

17. 18
Challenge Solution
Data diversity
Extensive data support
Work with signal, images, financial, textual, and others formats
Lack of domain tools
High-quality libraries
Industry-standard algorithms for Finance, Statistics, Signal,
Image processing & more
Time consuming
Interactive, app-driven workflows
Focus on machine learning, not programing
Select best model and easily fine-tune model parameter
Platform diversity
Run analytics anywhere
Code generation for embedded targets
Deploy to broad range of enterprise system architectures
Flexible architecture for customized workflows
Complete machine learning platform
MATLAB Strengths for Machine Learning

18. 19
Key Takeaways
Use apps in your workflow to
quickly compare and select
candidate algorithms
Use programmatic
workflows for fine-tuning
model parameters to
achieve robust performance
Use feature selection to get rid
of unnecessary features and
prevent overfitting
Use automatic code generation
to rapidly deploy your analytics
to embedded targets

19. 20
Integrate Analytics with Systems
MATLAB
Runtime
C, C ++ HDL PLC
Embedded Hardware
CUDA
MATLAB Analytics
run anywhere
C/C++ ++
Excel
Add-in Java
Hadoop/
Spark
.NET
MATLAB
Production
Server
Standalone
Application
Enterprise Systems
Python
Web Apps

20. 21
Machine Learning for Edge Analytics and Code Deployment
▪ SVM Class.
▪ Linear Class.
▪ Linear Regr.
▪ Generalized Linear Regr.
▪ Decision trees
▪ Ensembles for Class.
▪ Ensembles for Regr.
▪ SVM Regr.
▪ KNN Class.
▪ Gaussian Process Regr.
▪ Discriminant Analysis
Deploy trained models as standalone C/C++ code

21. 23
© 2019 The MathWorks, Inc.
Deep Learning
A Practical Approach in MATLAB

22. 24
What is Deep Learning?

23. 25
Deep learning is a type of machine learning in which a model learns to
perform tasks directly from image, time-series or text data.
Deep learning is usually implemented using a neural network
architecture.

24. 26
Example 1: Object recognition using deep learning
Training
(GPU)
Millions of images from 1000
different categories
Prediction
Real-time object recognition using
a webcam connected to a laptop

25. 27
Example 2: Detection and localization using deep learning
Regions with Convolutional Neural
Network Features (R-CNN)
Semantic Segmentation using SegNet

26. 28
Example 3: Analyzing signal data using deep learning
Signal Classification using LSTMs Speech Recognition using CNNs

27. 29
Why deep learning?
Fashion MNIST: The “Hello, World!” of deep learning
Transfer learning with CNNs
(optional) Semantic segmentation
(optional) Deep learning with time series data
Ground Truth Labeling for datasets
Everything else in deep learning…
Agenda

28. 30
Diverse Applications of Deep Learning
Iris Recognition – 99.4% accuracy1
1. Source: An experimental study of deep convolutional features for iris recognition Signal Processing in Medicine and Biology Symposium (SPMB), 2016 IEEE
Shervin Minaee ; Amirali Abdolrashidiy ; Yao Wang; An experimental study of deep convolutional features for iris recognition
2. "A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation" David Ribeiro, Andre Mateus, Jacinto C. Nascimento, and Pedro Miraldo
3. Deep Joint Rain Detection and Removal from a Single Image" Wenhan Yang, Robby T. Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan
Human Aware Navigation for Robots2
Rain Detection and Removal3
MatConvnet
MatCaffe
MatConvnet

29. 31
Can you tell the difference? Japanese or Blenheim Spaniel?
Blenheim Spaniel Japanese Spaniel
Source: ILSVRC ImageNet dataset

30. 32
Why is Deep Learning So Popular Now?
Source: ILSVRC Top-5 Error on ImageNet
Human
Accuracy

31. 33
Deep Learning Enablers
• Labeled public datasets
• Increased GPU acceleration
• World-class models
AlexNet
PRETRAINED
MODEL
Caffe
I M P O R T E R
ResNet-50
PRETRAINED MODEL
TensorFlow-
Keras
I M P O R T E R
VGG-16
PRETRAINED
MODEL
GoogLeNet
PRETRAINED
MODEL
ResNet-101
PRETRAINED MODEL
Inception-v3
M O D E L S

32. 34
Machine Learning vs Deep Learning
Deep learning performs end-to-end learning by learning features, representations and tasks directly
from images, text and sound
Deep learning algorithms also scale with data – traditional machine learning saturates
Machine Learning
Deep Learning

33. 35
Deep Learning Workflow
Files
Databases
Sensors
ACCESS AND EXPLORE
DATA
DEVELOP PREDICTIVE
MODELS
Hardware-Accelerated
Training
Hyperparameter Tuning
Network Visualization
LABEL AND PREPROCESS
DATA
Data Augmentation/
Transformation
Labeling Automation
Import Reference
Models
INTEGRATE MODELS WITH
SYSTEMS
Desktop Apps
Enterprise Scale Systems
Embedded Devices and
Hardware

34. 36
Why deep learning?
Fashion MNIST: The “Hello, World!” of deep learning
Transfer learning with CNNs
(optional) Semantic segmentation
(optional) Deep learning with time series data
Ground Truth Labeling for datasets
Everything else in deep learning…
Agenda

35. 37
Fashion-MNIST Dataset
What?
A collection if items
such as bags, shoes,
etc.
Why?
Benchmark machine
learning algorithms
How many?
60,000 training images
10,000 test images
Best results? 96.3% accuracy
Sources: https://github.com/zalandoresearch/fashion-mnist

36. 38
Convolutional Neural Networks (CNN)
“Deep” in deep learning
refers to number of layers

37. 39
Long Short Term Memory Networks
▪ Recurrent Neural Network that carries a memory cell throughout the
process
▪ Sequence Problems
c0 C1 Ct

38. 40
Types of Datasets
Numeric
Data
Time Series/
Text Data
Image
Data
Machine Learning or
LSTM LSTM or CNN
CNN

39. 41
Why deep learning?
Fashion MNIST: The “Hello, World!” of deep learning
Transfer learning with CNNs
(optional) Semantic segmentation
(optional) Deep learning with time series data
Ground Truth Labeling for datasets
Everything else in deep learning…
Agenda

40. 42
Why deep learning?
Fashion MNIST: The “Hello, World!” of deep learning
Transfer learning with CNNs
(optional) Semantic segmentation
(optional) Deep learning with time series data
Ground Truth Labeling for datasets
Everything else in deep learning…
Agenda

41. 43
Two Approaches for Deep Learning
2. Fine-tune a pre-trained model (transfer learning)
1. Train a Deep Neural Network from Scratch

42. 44
Transfer Learning Workflow
Early layers learn low-
level features (edges,
blobs, colors)
Last layers
learn task-
specific features
1 million images
1000s classes
Load pretrained network

43. 45
Transfer Learning Workflow
Early layers that learned
low-level features
(edges, blobs, colors)
Last layers that
learned task
specific features
1 million images
1000s classes
Load pretrained network
Fewer classes
Learn faster
New layers learn
features specific
to your data
Replace final layers

44. 46
Transfer Learning Workflow
Early layers that learned
low-level features
(edges, blobs, colors)
Last layers that
learned task
specific features
1 million images
1000s classes
Load pretrained network
Fewer classes
Learn faster
New layers to learn
features specific
to your data
Replace final layers
100s images
10s classes
Training images
Training options
Train network

45. 47
Fewer classes
Learn faster
New layers learn
features specific
to your data
Replace final layers
Transfer Learning Workflow
Early layers that learned
low-level features
(edges, blobs, colors)
Last layers that
learned task
specific features
1 million images
1000s classes
Load pretrained network
Fewer classes
Learn faster
New layers to learn
features specific
to your data
Replace final layers
100s images
10s classes
Training images
Training options
Train network
Test images
Trained Network
Predict and assess
network accuracy

46. 48
Transfer Learning Workflow
Early layers that learned
low-level features
(edges, blobs, colors)
Last layers that
learned task
specific features
1 million images
1000s classes
Load pretrained network
Fewer classes
Learn faster
New layers to learn
features specific
to your data
Replace final layers
100s images
10s classes
Training images
Training options
Train network
Test images
Trained Network
Predict and assess
network accuracy
Test images
Trained Network
Predict and assess
network accuracy
Probability
Boat
Plane
Car
Train
Deploy results

47. 49
Transfer Learning Workflow
Probability
Boat
Plane
Car
Train
Deploy results
Early layers that learned
low-level features
(edges, blobs, colors)
Last layers that
learned task
specific features
1 million images
1000s classes
Load pretrained network
Fewer classes
Learn faster
New layers to learn
features specific
to your data
Replace final layers
100s images
10s classes
Training images
Training options
Train network
Test images
Trained Network
Predict and assess
network accuracy

48. 50
Example: Food classifier using deep transfer learning
5 Category
Classifier
Caesar salad
French fries
Burgers
Pizza
Sushi

49. 51
Transfer Learning Workflow
Probability
Boat
Plane
Car
Train
Deploy results
Early layers that learned
low-level features
(edges, blobs, colors)
Last layers that
learned task
specific features
1 million images
1000s classes
Load pretrained network
Fewer classes
Learn faster
New layers to learn
features specific
to your data
Replace final layers
100s images
10s classes
Training images
Training options
Train network
Test images
Trained Network
Predict and assess
network accuracy

50. 52
Why Perform Transfer Learning
▪ Requires less data and training time
▪ Reference models (like AlexNet,
VGG-16, VGG-19, Inception-v3) are
great feature extractors
▪ Leverage best network types from
top researchers
(list of all models)
AlexNet
PRETRAINED
MODEL
Caffe
I M P O R T E R
ResNet-50
PRETRAINED MODEL
TensorFlow-
Keras
I M P O R T E R
VGG-16
PRETRAINED
MODEL
GoogLeNet
PRETRAINED
MODEL
ResNet-101
PRETRAINED MODEL
Inception-v3
M O D E L S

51. 53
Import the Latest Models for Transfer Learning
Pretrained Models*
▪ AlexNet
▪ VGG-16
▪ VGG-19
▪ GoogLeNet
▪ Inception-v3
▪ ResNet50
▪ ResNet-101
▪ Inception-resnet-v2
▪ SqueezeNet
▪ MobileNet (coming soon)
* single line of code to access model
Import Models from Frameworks
▪ Caffe Model Importer
▪ TensorFlow-Keras Model Importer
▪ ONNX - Importer/ Exporter
AlexNet
PRETRAINED
MODEL
Caffe
I M P O R T E R
ResNet-50
PRETRAINED MODEL
TensorFlow-
Keras
I M P O R T E R
VGG-16
PRETRAINED
MODEL
GoogLeNet
PRETRAINED
MODEL
ResNet-101
PRETRAINED MODEL
Inception-v3
M O D E L S

52. 54
Interoperability with Deep Learning Frameworks
▪ Import and export models using
the Open Neural Network Exchange
(ONNX) format
▪ Model importers
– Caffe
– TensorFlow-Keras
▪ Access pretrained models
with a single line of code
Sep ‘17 Mar ‘18
Mar ‘17
Sep ‘16 Sep ‘18
ONNX
PyTorch
MATLAB
MXNet
Caffe2 TensorFlow
Chainer
Core ML
Cognitive
Toolkit
Keras-
Tensorflow
Caffe

53. 55
A short recap…

54. 56
What we covered in this section…
▪ Transfer learning
– Easily modify existing networks with one-line commands
▪ Access large datasets
– Using the imageDatastore
▪ Visualize and Analyze Networks
– Using the Network Analyzer
▪ Make training faster
– Freezing the layers
▪ Improve training results
– Data augmentation with augmentedImageDataStore
– Automatic parameter selection with Bayesian hyperparameter tuning

55. 58
Why deep learning?
Fashion MNIST: The “Hello, World!” of deep learning
Transfer learning with CNNs
(optional) Semantic segmentation
(optional) Deep learning with time series data
Ground Truth Labeling for datasets
Everything else in deep learning…
Agenda

56. 59
Why deep learning?
Fashion MNIST: The “Hello, World!” of deep learning
Transfer learning with CNNs
(optional) Semantic segmentation
(optional) Deep learning with time series data
Ground Truth Labeling for datasets
Everything else in deep learning…
Agenda

57. 60
What is semantic segmentation?

58. 61
Original Image
ROI detection
Pixel classification

59. 62
Semantic Segmentation
CamVid Dataset
1. Segmentation and Recognition Using Structure from Motion Point Clouds, ECCV 2008
2. Semantic Object Classes in Video: A High-Definition Ground Truth Database ,Pattern Recognition Letters

60. 63
Semantic Segmentation Network
Boat
Airplane
Other classes

61. 64
Semantic Segmentation Network

62. 65
Useful Tools for Semantic Segmentation
▪ Automatically create network structures
– Using segnetLayers and fcnLayers
▪ Handle pixel labels
– Using the pixelLabelImageDatastore and
pixelLabelDatastore
▪ Evaluate network performance
– Using evaluateSemanticSegmentation
▪ Examples and tutorials to learn concepts

63. 66
Why deep learning?
Fashion MNIST: The “Hello, World!” of deep learning
Transfer learning with CNNs
(optional) Semantic segmentation
(optional) Deep learning with time series data
Ground Truth Labeling for datasets
Everything else in deep learning…
Agenda

64. 67
Why deep learning?
Fashion MNIST: The “Hello, World!” of deep learning
Transfer learning with CNNs
(optional) Semantic segmentation
(optional) Deep learning with time series data
Ground Truth Labeling for datasets
Everything else in deep learning…
Agenda

65. 68
New Product: Text Analytics Toolbox
▪ Text extraction from PDF and Microsoft Word files
▪ Text preprocessing and normalization
▪ TF-IDF and word frequency statistics
▪ Machine learning algorithms, including Latent Dirichlet
Allocation (LDA) and Latent Semantic Analysis (LSA)
▪ Word-embedding training, and pretrained model
import with word2vec, FastText, and GloVe
▪ Word cloud and text scatter plots
Analyze and model text data

66. 70
Time Series Classification (Human Activity Recognition)
Long short-term memory networks
▪ Dataset is accelerometer and gyroscope
signals captured with a smartphone
▪ Data is a collection of time series with 9
channels
Deep
Learning
sequence to one

67. 71
LSTM sequences
Sequence 1 Sequence 2 Sequence (n-1)
1
2
3
4
5
6
7
8
9
2.5 sec 2.5 sec 2.5 sec
……………………………...........
……………………………...........
Standing Walking Sitting
Time

68. 72
Why deep learning?
Fashion MNIST: The “Hello, World!” of deep learning
Transfer learning with CNNs
(optional) Semantic segmentation
(optional) Deep learning with time series data
Ground Truth Labeling for datasets
Everything else in deep learning…
Agenda

69. 73
Why deep learning?
Fashion MNIST: The “Hello, World!” of deep learning
Transfer learning with CNNs
(optional) Semantic segmentation
(optional) Deep learning with time series data
Ground Truth Labeling for datasets
Everything else in deep learning…
Agenda

70. 74
“I love to label and
preprocess my data”
~ Said no engineer, ever.

71. 75
Data
Apps for Labeling
“How do I label
my data?”
New App for
Ground Truth
Labeling
Label pixels
and regions for
semantic
segmentation

72. 76
Attributes and Sublabels NEW in

73. 77
Why deep learning?
Fashion MNIST: The “Hello, World!” of deep learning
Transfer learning with CNNs
(optional) Semantic segmentation
(optional) Deep learning with time series data
Ground Truth Labeling for datasets
Everything else in deep learning…
Agenda

74. 78
Why deep learning?
Fashion MNIST: The “Hello, World!” of deep learning
Transfer learning with CNNs
(optional) Semantic segmentation
(optional) Deep learning with time series data
Ground Truth Labeling for datasets
Everything else in deep learning…
Agenda

75. 79
Deploying Deep Learning Models for Inference
GPU
Coder
Deep Learning
Networks
NVIDIA
TensorRT &
cuDNN
Libraries
ARM
Compute
Library
Intel
MKL-DNN
Library

76. 80
Semantic Segmentation
Running in MATLAB Generated Code from GPU Coder

77. 81
Desktop CPU
Raspberry Pi board
Deploying to CPUs
GPU
Coder
Deep Learning
Networks
NVIDIA
TensorRT &
cuDNN
Libraries

78. 82
Training Performance and Scalability

79. 84
Deep Learning on CPU, GPU, Multi-GPU and Clusters
Single
CPU
Single CPU
Single GPU
HOW TO TARGET?
Single CPU, Multiple GPUs
On-prem server with
GPUs
Cloud GPUs
(AWS)

80. 85
Inference Performance and Deployment

81. 86
Using GPU Coder for Deep Learning
Access Data Preprocess Select Network Train
Image
Acquisition
Tbx
Image
Processing Tbx
Computer
Vision System
Tbx
Neural
Network Tbx
Parallel
Computing Tbx
GPU
Coder
Deploy

82. 87
Using CUDA v9
and cuDNN v7
The inference is for single image performance.

83. 89
Overview of deep learning deployment options
“How do I deploy
my model?”
Deploy / Share
▪ Create Desktop Apps
▪ Run Enterprise Solution
▪ Generate C and C++ Code
GPU Coder
▪ Target GPUs
Introducing:
GPU Coder-
Convert to
NVIDIA CUDA
code
▪ Generate C and C++ Code

84. 90
Other Features

85. 91
Designing and Building Deep Learning Models
▪ Edit and build deep networks
(Deep Network Designer app)
▪ Visualize, analyze, and find problems in network
architectures before training​​ (Network Analyzer)
▪ Automate ground-truth labeling using apps
– Image Labeler app
– Video Labeler app
– Audio Labeler app
▪ Monitor training progress with plots for accuracy,
loss, validation metrics, and more
▪ Visualize and debug deep learning models
+
Deep Learning Toolbox
Computer Vision System Toolbox
Audio System Toolbox

86. 92
Define new deep neural network layers

87. 93
Object Detection Frameworks
▪ Single line of code to train a detector
▪ Includes:
– R-CNN
– Fast R-CNN
– Faster R-CNN

88. 94
Deep learning features overview
▪ Classification
▪ Regression *
▪ Semantic segmentation
▪ Object detection *
▪ Scalability *
– Multiple GPUs
– Cluster or cloud
▪ Custom network layers *
▪ Import models *
– Caffe
– Keras/TensorFlow
▪ Data augmentation *
▪ Hyperparameter tuning *
– Bayesian optimization
▪ Python MATLAB interface *
▪ LSTM networks *
– Time series, signals, audio
▪ Custom labeling *
– API for ground-truth labeling
automation
– Superpixels
▪ Data validation *
– Training and testing
* We can cover in more detail outside this presentation

89. 95
Reinforcement Learning

90. 96
What is Reinforcement Learning?
▪ What is Reinforcement Learning?
– Type of machine learning that trains an
‘agent’ through repeated interactions with an
environment
▪ How does it work?
– Through a trial & error process that
maximizes success

91. 97
Reinforcement Learning Applications
▪ Why should you care about Reinforcement Learning?
– It enables the use of deep learning for controls and decision-making applications
Game Play
Controls
Robotics
Autonomous driving

92. 102
Reinforcement Learning Toolbox
New in R2019a
▪ Built-in and custom algorithms for reinforcement
learning
▪ Environment modeling in MATLAB and Simulink
▪ Deep Learning Toolbox support for designing policies
▪ Training acceleration through GPUs and cloud
resources
▪ Deployment to embedded devices and production
systems
▪ Reference examples for getting started

93. 105
Resources

94. 106
Learn More: Machine Learning
Documentation: mathworks.com/machine-learning

95. 107
Learn More: Big Data
▪ MATLAB Documentation
– Strategies for Efficient Use of Memory
– Resolving "Out of Memory" Errors
▪ Big Data with MATLAB
– www.mathworks.com/discovery/big-data-matlab.html
▪ Tall Arrays in Action
– https://www.mathworks.com/videos/matlab-tall-arrays-in-action-122883.html

96. 108
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