Deep Learning – With an Example Implementation

1. Deep Learning – With an Example Implementation Krishna Bhavsar VinayakJoglekar

2. KrishnakumarBhavsar More than 11 years working on natural language processing, social media analytics, text mining and machine learning in various industry domains Worked on most of the Open Source NLP libraries related to computational linguistics. Published a paper on sentiment analysis augmentation techniques in 2010 NAACL. Created an NLP pipeline/toolset and open sourced it for public use. Authored “Natural Language Processing Cookbook Using Python”

3. VinayakJoglekar Passionately building software products with Lean and Agile Teams Nurtured top notch software professionals and built several self organizing teams. Actively pursuing business opportunities in big data analytics, machine learning and natural language processing. Deep domain expertise in technical hiring including resume processing, technical assessment and interviews.

4. AI vs ML vs Deep Learning

5. Difference Between ML and Deep Learning • ML – the concept existed in the 70s and 80s with the old school statistical analysis, study of probabilities, regressions, • Concept of deep learning existed in the early 80s, only the invent of parallel computing and cheap processing power with commodity hardware has expedited the use of deep learning algorithms • Primary difference is the feedback mechanism involved with the Deep Learning algorithms that is absent in ML algorithms (it works in multiple passes)

6. Neural Networks Brain Neurons and Synapses model Two layer Feed Forward ANN

7. Example Problems that can be Solved • Speech Recognition • Natural Language Processing • Bioinformatics • Image Classification – classic cats and dogs classification problem • Edges • Shapes (circles, rectangles, squares, ellipses) • Whiskers, Ears, Eyes (definitive features) • Cat or Dog

8. Preprocessing • Image • Size standardization/Scaling • Color standardization • Text • Sentence Segmentation • Tokenization • Stemming/Lemmatization • Standardizations (case, special characters) • Statistical • Normalization • Outliers • Missing values

10. Identifying the Cat

11. Convolution

12. Convolution – Sliding Window

13. Defining the Resume Classification Problem • Continuous Stream of incoming resumes • Heterogeneous formats • Practically Unaccountable number of buckets • Manual labor to classify is immense

14. Any Typical Candidate Resume

15. Solution Deep Learning GATE Jape CVs

16. Modelling the Data • More than a thousand samples (CVs) • GATE(japes) gives all the pre-defined Entities and many more lower level feature details • Standardization of Resume Size • Working at offset level • Encoding 98 features per offset per CV

17. Precision and Recall

18. UnderfittingvsOverfitting

19. Precision vs Recall Conundrum Output Second attempt Output First attempt . . . . . . . . . . . Project9 . . . . . Project11 Project10 Project1 Project1 Project2 Project2 Project3 Project3 Project4 Project4 Project5 Project5 Project6 Project6 Project7 Project7 Project8 Project8

20. Results • 1st Pass • 90 features and 8 target variables • Smaller size target buckets were extremely accurate • Larger size target buckets had sprinkled outliers all over the place, unwarranted breaks and overlaps • 2nd Pass • Proximity, feature size in consecutive offsets and location as features and same 8 target variables • Outliers were largely gone, overlaps were completely eliminated • In terms of accuracy at the level of single offset the prediction error was only 12%

21. First Pass vs Second Pass Output Output Second Pass Output First Pass . . . . . . . . . . . Project . . Project . . . Project . . Project Project

22. Tools, Tech and Algorithms Used • Logistic Regression, Random Forest, Tensor MLP, Neural net Multinomial, SMOTE

23. Final Outcome with an Error Rate of 12.4%

24. Recent Book Authored by KrishnakumarBhavsar

25. sales@synerzip.com

26. Who is Synerzip Synerzip is your Agile software product co-development partner 2X accelerate product roadmap 110+ product success stories Inc. 5000 awarded Inc 5000 6 years in a row 500+ strong team DNA a truly agile product development partner 10+ years in business 50% savings from optimized delivery Dual-Shore matured delivery model

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29. Clients… …100 more

30. Next Webinar Is REACT the Best Thing Since Sliced Bread? Tuesday, May 15, 2018 at 10am CT presenter: Yogesh Patel, Author and Director of Engineering at Synerzip .

31. Thank You

32. Final outcome(use if required) • Full Name - 1st Pass, location check • Objective - 2nd Pass • Overview – 1st Pass • Education – 1st Pass, pure • Company Experience – 1st Pass • Skillset – 1st Pass, pure • Project – 2nd Pass, pure • Footnote – 2nd Pass, pure