This presentation briefs about machine learning technologies, its various learning methodologies, its types. Also it briefs about the Open Computer Vision, Graphics Processing Unit and CUDA Frameworks.

1. Machine Learning
Overview

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

8. Block Diagram
Evaluation
Data
Object
Model
Test
Prediction

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

22. Thank You