Recommender Systems Hands on Workshop | Matrix-Based Recommender Systems | Day 1

Session Overview:

This online workshop consists of presentations & labs covering the fundamental techniques & tools for building highly effective recommender systems from matrix based recommender systems to wide & deep network models. 

Recommender systems aim to predict users' interests and recommend product items that quite likely are interesting for them. The secret to tailored online experiences and powerful decision-support tools across industries like finance, retail, healthcare, etc. are recommender systems.  

Prerequisites:
-Intermediate knowledge of Python, including understanding of list comprehension
-Data science experience using Python
-Familiarity with NumPy and matrix mathematics

Registration and attendance at Day 1 and Day 2 is recommended to receive a FREE certification.
If you have not done so, register for Day 2 here - https://membership.anitab.org/event/Recommender_Day2

This event is for Premium Student Members Only.  

Day 1:

Introduction (15 min)

-Meet the instructor.
-Create an account at courses.nvidia.com/join

Matrix-Based Recommender Systems (120 mins) 

-Implement collaborative filtering with singular value decomposition (SVD):
-Read sparse data into a GPU using CuPy.
-Perform ALS efficiently with NumPy broadcasting rules.
-Build a content-based filter with cuDF.

Sai Sharanya Nalla is a Principal Data Scientist at Nike, New York, where she enjoys working in the intersection of AI and Sport Science. She delivers differentiating consumer facing services & capabilities across Nike's fitness apps leveraging Data Science & Machine Learning. She is an expert in building high throughput, scalable, and cost-effective MLOps solutions having worked at AWS & American Express.

Sai is passionate about recruiting and professional development of women in the Data Science field. Sai has been actively supporting the next generation of young women at Rewriting the Code (RTC) as Data Science Mentor & serving in AnitaB.org Artificial Intelligence Committee to develop thought leadership on the practical use as well as ethical implications of advances in artificial intelligence and explore ways to encourage women to be active participants in the AI community 

In her spare time, she enjoys listening to podcasts, publishing tech blogs, going for long walks and kayaking. Find her on LinkedIn - https://www.linkedin.com/in/sharanyanalla/

Shawn McCarthy is responsible for leading the GWAM Technology Architecture, Big Data and Risk teams, managing relationships with key stakeholders, driving and championing change while managing large enterprise-wide initiatives. Shawn plays a key role in accelerating the transformation of GWAM Technology by leading and developing a ‘niche’ technical team and provides a deep technical focus on global WAM platforms and architecture capabilities. 

Shawn holds a Bachelor of Science and a Masters of Science from the University of Colorado, and is currently pursuing a PhD CSIS. He is a member of the computer science and engineering advisory board, he designed (and teaches) a Web API course and he supports the CU Denver student chapters of the Association for Computing Machinery (ACM) and the Society of Women Engineers (SWE).  Shawn has served as co-chair on the AnitaB.org Mentoring Committee and currently serves as member on the AnitaB.org Artificial Intelligence Committee to develop thought leadership on the practical use as well as ethical implications of advances in artificial intelligence and explore ways to encourage women to be active participants in the AI community.

He hopes to inspire success in current students so they get as much out of their education as he did. "I find it rewarding to be able to give back to academia which has provided me with opportunities and experiences that I cherish and I am grateful for.".