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Machine Learning
Overview
Overview
• An Artificial Intelligence (AI) technique which
provides the system to learn by itself.
• Machine acts without being explicitly
programmed
• Utilizes the historical data to make better
business decisions
• Evolved from pattern recognition and
computation theory of Artificial Intelligence
• Basically an algorithm not a magic
Application Areas
• Self driving cars
• Practical speech recognition
• Efficient web search
• Human genome understanding
• Object detection
• Face detection and recognition
• Vehicle monitoring for CCTV
• Email spam detection / Cyber fraud detection
• Online recommendation
When to use
• Cannot code the rules
– Scenario which cannot be solved using a
deterministic rule based solution
– Rule depends on too many factors
– Many rules overlap and needs to be fine tuned
– Difficult scenario for a human to code the rules
• Cannot scale
– Effective to handle large scale problems
Programming Language
• MATLAB
– Excellent tool for representation and working with
matrices
• R
– Platform used to understand and explore the data
using statistical methods and graphs
• Python
– Popular scientific language and rising star of ML.
• JAVA/C
ML Types
Labeled Data
Labeled Data
Unlabeled Data
Unlabeled Data
Model
Model
Model
Different Types
• Supervised Learning
– Analyses the training data and produces output
accordingly
– Algorithm iteratively makes predictions on the training
data
– Neural Networks, Multi Layer Perception, Decision Trees
• Un supervised Learning
– Learn to inherent structure from input data
– Clustering, Distances and Normalization, Self Organizing
maps.
• Semi – Supervised
– Mixture of supervised and un-supervised techniques
Block Diagram
Evaluation
Data
Object
Model
Test
Prediction
Process Involved
• Data Collection
• Data Preparation
• Model Selection
• Training
• Evaluation
• Prediction
Training Process
• Input training data source
• Name of the attribute that contains target to
be predicted
• Required data transformation instructions
• Parameters to control the learning algorithm
Model
• Refers to the model artifact created by the
training process
• Provides the ML algorithm with training data
to learn from.
• Model Zoo
– Created with multiple datasets like COCO, Kitti and
OpenImages
Models
• Binary Classification Model
– Predicts a binary outcome ( one of two possible
classes )
• Multi class Classification Model
– Generates predictions for multiple classes
• Regression Model
– Predicts a numeric value
– How many units will sell tomorrow
Dataset
• Training set
– Set of examples used for learning with known
target
• Validation set
– Set of examples used to fine tune the classifier
and estimate the error
• Test set
– Used to access the performance of the classifier
Machine Learning Frameworks
• H2o.ai
• Apache Singa
• Amazon Machine Learning
• Azure ML Studio
• Massive Online Analysis
• Mlpack
• Spark Mlib
• Tensorflow
• Caffe2
ML Examples
• Object Detection & Recognition
• Multi Vehicle / Car Detection
• Vehicle Speed detection
OpenCV
• Open Source Computer Vision Library
• Library functions mainly aimed at real-time
computer vision, image processing and
machine learning
• Has C++, JAVA, Python library interface
• Now features GPU Acceleration for real time
operations
GPU
• Graphic Processing Unit
• Used to render 3D graphics comprised on
polygons
• Technologies like OpenCV, OpenCL, CUDA used to
assist the GPU in non-graphics computations
• Improves the overall performance of the
computer
• Used to accelerate the deep learning, analytics
and engineering applications.
CUDA
• Parallel computing platform and programming
model developed by NVIDIA
• Able to speed up the computing applications
by harnessing the power of GPUs
• GPU accelerated computing
– Sequential part of workload runs on CPU
– Intensive portion of application runs on thousands
of GPU cores in parallel
Tensorflow
• Open source machine learning framework for
everyone
• Numerical computation using data flow graphics
• Deploy computation on one or more CPUs or
GPUs in desktop
• Developed by Google
• Also supports hardware acceleration with
Android Neural Networks APIs.
Combinations
• Tensorflow 1.4
– Nvidia CUDA 8.0
• Tensorflow 1.5
– Nvidia CUDA 9.0
References
• https://docs.aws.amazon.com/machine-
learning/latest/dg/
• https://developers.google.com/machine-
learning/crash-course/
• https://en.wikipedia.org/wiki/Machine_learni
ng
• https://www.coursera.org/learn/machine-
learning
Thank You

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Introduction to Machine learning

  • 2. Overview • An Artificial Intelligence (AI) technique which provides the system to learn by itself. • Machine acts without being explicitly programmed • Utilizes the historical data to make better business decisions • Evolved from pattern recognition and computation theory of Artificial Intelligence • Basically an algorithm not a magic
  • 3. Application Areas • Self driving cars • Practical speech recognition • Efficient web search • Human genome understanding • Object detection • Face detection and recognition • Vehicle monitoring for CCTV • Email spam detection / Cyber fraud detection • Online recommendation
  • 4. When to use • Cannot code the rules – Scenario which cannot be solved using a deterministic rule based solution – Rule depends on too many factors – Many rules overlap and needs to be fine tuned – Difficult scenario for a human to code the rules • Cannot scale – Effective to handle large scale problems
  • 5. Programming Language • MATLAB – Excellent tool for representation and working with matrices • R – Platform used to understand and explore the data using statistical methods and graphs • Python – Popular scientific language and rising star of ML. • JAVA/C
  • 6. ML Types Labeled Data Labeled Data Unlabeled Data Unlabeled Data Model Model Model
  • 7. Different Types • Supervised Learning – Analyses the training data and produces output accordingly – Algorithm iteratively makes predictions on the training data – Neural Networks, Multi Layer Perception, Decision Trees • Un supervised Learning – Learn to inherent structure from input data – Clustering, Distances and Normalization, Self Organizing maps. • Semi – Supervised – Mixture of supervised and un-supervised techniques
  • 9. Process Involved • Data Collection • Data Preparation • Model Selection • Training • Evaluation • Prediction
  • 10. Training Process • Input training data source • Name of the attribute that contains target to be predicted • Required data transformation instructions • Parameters to control the learning algorithm
  • 11. Model • Refers to the model artifact created by the training process • Provides the ML algorithm with training data to learn from. • Model Zoo – Created with multiple datasets like COCO, Kitti and OpenImages
  • 12. Models • Binary Classification Model – Predicts a binary outcome ( one of two possible classes ) • Multi class Classification Model – Generates predictions for multiple classes • Regression Model – Predicts a numeric value – How many units will sell tomorrow
  • 13. Dataset • Training set – Set of examples used for learning with known target • Validation set – Set of examples used to fine tune the classifier and estimate the error • Test set – Used to access the performance of the classifier
  • 14. Machine Learning Frameworks • H2o.ai • Apache Singa • Amazon Machine Learning • Azure ML Studio • Massive Online Analysis • Mlpack • Spark Mlib • Tensorflow • Caffe2
  • 15. ML Examples • Object Detection & Recognition • Multi Vehicle / Car Detection • Vehicle Speed detection
  • 16. OpenCV • Open Source Computer Vision Library • Library functions mainly aimed at real-time computer vision, image processing and machine learning • Has C++, JAVA, Python library interface • Now features GPU Acceleration for real time operations
  • 17. GPU • Graphic Processing Unit • Used to render 3D graphics comprised on polygons • Technologies like OpenCV, OpenCL, CUDA used to assist the GPU in non-graphics computations • Improves the overall performance of the computer • Used to accelerate the deep learning, analytics and engineering applications.
  • 18. CUDA • Parallel computing platform and programming model developed by NVIDIA • Able to speed up the computing applications by harnessing the power of GPUs • GPU accelerated computing – Sequential part of workload runs on CPU – Intensive portion of application runs on thousands of GPU cores in parallel
  • 19. Tensorflow • Open source machine learning framework for everyone • Numerical computation using data flow graphics • Deploy computation on one or more CPUs or GPUs in desktop • Developed by Google • Also supports hardware acceleration with Android Neural Networks APIs.
  • 20. Combinations • Tensorflow 1.4 – Nvidia CUDA 8.0 • Tensorflow 1.5 – Nvidia CUDA 9.0
  • 21. References • https://docs.aws.amazon.com/machine- learning/latest/dg/ • https://developers.google.com/machine- learning/crash-course/ • https://en.wikipedia.org/wiki/Machine_learni ng • https://www.coursera.org/learn/machine- learning