The document summarizes deep learning technology and the COCO framework. It discusses that deep learning is a subdomain of machine learning that uses neural networks to process large amounts of data. The COCO framework was introduced by Microsoft to recognize 2D and 3D objects in images using a hierarchical labeling approach based on blockchain technique. The COCO dataset contains over 2.5 million labeled instances across 328,000 images for 91 object categories. Future applications of deep learning and blockchain are discussed, such as in banking, finance, and internet hosting.

1. 3
THE DEEP LEARNING TECHNOLOGY ON COCO
FRAMEWORK
Jamia institute of engineering and management studies,akkalkuwa
Kavayitri Bahinabai Chaudhari North Maharashtra University, Jalgaon

3. content

4. introduction
Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t
understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years.
-Mark Cuban
(Feb 8, 2017)

5. Deep learning
Neural network
Linear regression

6.  Machine learning is the sub-domain of the artificial intelligence, and deep
learning is the also sub-domain of the machine learning.
 Deep learning is next stage of evolution of machine learning.
 It deals with too mathematics and too much of code.
 Deep learning algorithm needs large amount of data for processing.
 There are several theories about how Deep Learning works that make
sense to data scientists, programmers, statisticians, mathematicians, etc.
WHAT IS DEEP LEARNING?

7. What is deep learning neural network?
Neuron is the basic elements of the neural network
Neuron process according to input and generate output
Same scenario for neural network
After generating output it send other neuron for further process or generate
final output

8. Linear regression
 Regression is a method of
modelling a target value based
on independent predictors
 It create relationship between
independent and dependent
variable

9. Linear regression
Y=b0+b1X

10. The coco framework
 The Microsoft Common Objects in Context (MS
COCO)
 Introduce by Microsoft
 Recognized the object on a pictures.
 2D and 3D objects.
 Containing particular object categories using a
hierarchical labeling approach
 Based on blockchain technique

11. Data set of COCO
 91 objects recognized by a 4 year child
 Dataset has 2,500,000 labeled instances
in 328,000 images
 Each category found, the individual
instances were labeled, verified, and finally
segmented

12. Objective of datasets
Image/object classification
Object localization
Semantic segmentation

13. iconic object images
iconic scene images
non-iconic images

14. Our annotation pipeline is split into 3 primary tasks
Image annotation

15. “
”
Advantages

16. Disadvantages

17. Future scope
BLOCKCHAIN TECHNOLOGY MARKET
2018 FUTURE SCOPE AND INDUSTRY SIZE
PROJECTED TO RAISE USD $2 BILLION
WITH 51% OF CAGR BY 2022
Internet hosting service GitHub
Banking sector
Finance sector Insurance sector

18. Future scope

19. References
[1] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,”
in CVPR, 2009.
[2] M. Everingham, L. Van Gool, C. K. I.Williams, J.Winn, and A. Zisserman, “The PASCAL visual object classes (VOC)
challenge,” IJCV, vol. 88, no. 2, pp. 303–338, Jun. 2010.
[3] J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba, “SUN database: Large-scale scene recognition from abbey to
zoo,” in CVPR, 2010.
[4] P. Doll´ar, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,” PAMI, vol.
34, 2012.
[5] A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet classification with deep convolutional neural networks,” in
NIPS, 2012.
[6] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic
segmentation,” in CVPR, 2014.
[7] P. Sermanet, D. Eigen, S. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “OverFeat: Integrated recognition, localization
and detection using convolutional networks,” in ICLR, April 2014.
[8] A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth, “Describing objects by their attributes,” in CVPR, 2009.

20. Have you any question?
Thank you for listening