Lifting Up: Deep Learning for implementing anti-hunger and anti-poverty programs-- TensorFlow Python Library
usha rengaraju (~usha75) |
TensorFlow Python library has been you in implementation . This proposal has been selected at ODSC India 2019 where the colloborators are Professor Dr.Badri Narayanan Gopalakrishnan , Professor at University of Washington and Shalini Sinha , Director of Data Science , Numerify.
Ending poverty and zero hunger are top two goals United Nations aims to achieve by 2030 under its sustainable development program. Hunger and poverty are byproducts of multiple factors and fighting them require multi-fold effort from all stakeholders. Artificial Intelligence and Machine learning has transformed the way we live, work and interact. However economics of business has limited its application to few segments of the society. A much conscious effort is needed to bring the power of AI to the benefits of the ones who actually need it the most – people below the poverty line. Here we present our thoughts on how deep learning and big data analytics can be combined to enable effective implementation of anti-poverty programs. The advancements in deep learning , micro diagnostics combined with effective technology policy is the right recipe for a progressive growth of a nation. Deep learning can help identify poverty zones across the globe based on night time images where the level of light correlates to higher economic growth. Once the areas of lower economic growth are identified, geographic and demographic data can be combined to establish micro level diagnostics of these underdeveloped area. The insights from the data can help plan an effective intervention program. Machine Learning can be further used to identify potential donors, investors and contributors across the globe based on their skill-set, interest, history, ethnicity, purchasing power and their native connect to the location of the proposed program. Adequate resource allocation and efficient design of the program will also not guarantee success of a program unless the project execution is supervised at grass-root level. Data Analytics can be used to monitor project progress, effectiveness and detect anomaly in case of any fraud or mismanagement of funds.
Outline/Structure of the Case Study Introducing Poverty Trap Deep Learning framework to Identify Underdeveloped Areas Micro-level diagnostics framework using Machine Learning and Big data Analytics Key Insights for Intervention Programs Machine Learning and Big Data for donors and volunteers lead generation and conversion - key data-sets Data capturing for Future of research in Poverty and Hunger eradication Conclusion
Learning Outcome : Power of satellite image processing to identify the lower economy zones Understanding how demographic and geographic data can be used to gather micro level insights from these poverty zones. Application of Machine learning for fund growth and transparent fund management
Target Audience: Volunteers and NGOs looking for technology solutions to make their programs more effective and efficient,Data Scientists, Data Analysts, Deep Learning Engineers, Machine Learning Engineers, Economists , Technology Policy Makers, Intervention Design Engineers, Social Scientists.
Prerequisites for Attendees Curiosity Empathy Familiarity with Transfer Learning and Deep Learning basic concepts. (Not mandatory though )
Usha gave a talk at Google Developers Group on the same topic on International Women's day Bengaluru 2019 event and received overwhelming response for the talk.
I am a polymath and unicorn data scientist with strong foundations in Economics, Finance, Business Foundations, Business Analytics and Psychology. I specialize in Probabilistic Graphical Models, Machine Learning and Deep Learning. I have completed Financial Engineering and Risk Management program from Columbia University with top honors, micromasters in Marketing Analytics from UC Berkeley and statistical analysis in Life Sciences specialization from Harvard. I am chapter lead/Co-Organizer of Women in Machine Learning and Data Science Bengaluru Chapter and Core oganizing team member at WIDS Bengaluru .I have around 6 years of technical experience working in various companies like Infosys, Temenos, NeoEYED and Mysuru Consulting Group. I am part of dedicated group of experts and enthusiasts who explore Coursera courses before they open to the public, an ambassador at AIMed (an initiative which brings together physicians and AI experts), part time Data science instructor, mentor at GLAD (gladmentorship.com), mentor at JobsForHer and volunteer at Statistics without Borders. I developed the course curriculum for Probabilistic Graphical Models @ Upgrad which is taught by Professor Srinivasa Raghavan from IIIT Bangalore.
This proposal got accepted at ODSC India 2019
Speaker at World Machine Learning Summit 2018: https://1point21gws.com/machinelearning/bangalore/schedule.html#day1
Speaker at Google Cloud Next 18:
Session on Neuroeconomics -Neuroscience of Decision Making
Workshop at ODSC -Introduction to Bayesian Networks :
Speaker at BRUG (Time series Analysis):
Speaker at BangPypers: (Introduction to Probabilistic Graphical Models")
Speaker at PyLadies : https://twitter.com/aartee_ty/status/1066228144003264512
Workshop in Stock Price Prediction using Probabilistic Graphical Models https://www.meetup.com/Byte-Academy-Bangalore/events/256804439/
Webinar in Stock Price Prediction using Probabilistic Graphical Models https://www.meetup.com/Byte-Academy-Bangalore/events/fznzmqyzcbfb/