Machine Learning Vs. Deep Learning – An Example Implementation
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The document discusses deep learning's implementation in natural language processing and features contributions from experienced professionals in the field. It highlights key differences between machine learning and deep learning, demonstrates various applications, and outlines the challenges of resume classification. The results of a project are presented, showcasing improved prediction accuracy through multiple algorithmic passes.
Machine Learning Vs. Deep Learning – An Example Implementation
- 1. Deep Learning – With an Example Implementation Krishna Bhavsar Vinayak Joglekar
- 2. • 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” Krishnakumar Bhavsar
- 3. • 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. Vinayak Joglekar
- 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 Two layer Feed Forward ANN Brain Neurons and Synapses model
- 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
- 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
- 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
- 19. Precision vs Recall Conundrum Project3 Project4 Project5 . . . . . Output First attempt Output Second attempt . . . . . . . . . . . Project2 Project1 Project6 Project7 Project8 Project3 Project4 Project5 Project2 Project1 Project6 Project7 Project8 Project9 Project10 Project11
- 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 Project Project Project Project . . . . . . . Output First Pass Project Output Second Pass . . . . . . . . . . .
- 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% First Pass Second Pass First Name Objective Overview Project Education Footnote Company Experience Skillset
- 24. Recent Book Authored by Krishnakumar Bhavsar
- 25. © 2018 Synerzip© 2018, Synerzip Texas Tel: +1.469.374.0500 | Fax: +1.469.322.0490 Silicon Valley Tel: +1.510.519.9673 | Fax: +1.510.519.9673 India Tel: +91.20.67283222 | Fax: +91.20.67283222 sales@synerzip.com
- 26. © 2018 Synerzip26 Who is Synerzip Synerzip is your Agile software product co-development partner 500+ strong team Dual- Shore matured delivery model 110+ product success stories Inc. 5000 awarded Inc 5000 6 years in a row 10+ years in business 50% savings from optimized delivery DNA a truly agile product development partner 2X accelerate product roadmap
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- 31. Thank You
- 32. Final outcome(use if required) 1. Full Name - 1st Pass, location check 2. Objective - 2nd Pass 3. Overview – 1st Pass 4. Education – 1st Pass, pure 5. Company Experience – 1st Pass 6. Skillset – 1st Pass, pure 7. Project – 2nd Pass, pure 8. Footnote – 2nd Pass, pure