ODSC APAC Virtual Conference 2020 | December 8 - 9, 2020

Machine Learning & Deep Learning Track

Learn the latest models, advancements, and trends from the top practitioners behind two of data science’s hottest topics
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Machine Learning & Deep Learning

The Machine Learning & Deep Learning Conference is where experts in the rapidly expanding fields of Deep Learning and Machine Learning gather to discuss the latest advances, trends, and models in this exciting field.

Attend talks, tutorials, and workshops and hear from the creators and top practitioners as they demonstrate and teach the latest developments in Machine Learning and Deep Learning that solve problems in business and society today.

What You'll Learn

Talks & Workshops on these topics:

Topics

  • Machine Learning

  • Deep Learning

  • Artificial Intelligence

  • Neural Networks

  • Natural Language Processing

  • Computer Vision

  • Pattern Recognition

Tools & Languages

  • R

  • Python SciPy, Pandas, etc

  • Scikit-learn

  • Tensorflow

  • Spark

  • MLlib

  • H20

Tools & Languages

  • WEKA

  • Pylearn2

  • Theano

  • Caffe

  • Torch

  • Azure Machine Learning API

  • and many more..

ODSC APAC Virtual Conference 2020 | December 8 - 9, 2020

Register Now

Some Previous Speakers

Professor, ResearcherUniversity of Wisconsin-Madison

Sebastian Raschka, PhD

Sebastian Raschka is a machine learning researcher developing new deep learning architectures to solve problems in the field of biometrics with a focus on face recognition and privacy protection. Among others, his research activities include applications of machine learning to solve problems in (computational) biology. After receiving his doctorate from Michigan State University, Sebastian recently joined the University of Wisconsin-Madison as Assistant Professor of Statistics. Sebastian Raschka is also the author of the bestselling book “Python Machine Learning”, which received the ACM Best of Computing award in 2016 and was translated into many different languages, including German, Korean, Chinese, Japanese, Russian, Polish, and Italian. In his free time, Sebastian loves to contribute to open source projects, and methods that he implemented are now successfully used in machine learning competitions such as Kaggle.

Machine Learning and Artificial Intelligence in 2020: Recent Trends, Technologies, and Challenges(Talk)

Data ScientistVeeva Systems

Sihem Romdhani

Sihem Romdhani received her MASc degree in Machine Learning from the department of Electrical and Computer Engineering at the University of Waterloo-Canada, where her research was focused on Deep Learning for Speech Recognition. She is currently working with Veeva Systems as a Data Scientist, where she is building ML models for Natural Language Processing. She has led multiple projects on text parsing, sequence tagging, and information extraction from unstructured text data. She has also worked on recommendation systems using different ML algorithms including Reinforcement Learning. Sihem is very interested in AI and how to solve new and challenging problems. Throughout her education, academic research, and work in industry, she gathered experiences and knowledge that she enjoys sharing by actively doing public presentations.

Self-Supervised Learning and Natural Language Processing for Hate Speech Detection(Talk)

Deep Learning and Software GPU expert Nir Shavit
CEO, ProfessorNeural Magic, MIT

Nir Shavit, PhD

Nir Shavit is the CEO of Neural Magic, a machine learning startup that is currently in stealth mode and is a professor in the Department of Electrical Engineering and Computer Science at MIT. Shavit is a co-author of the book The Art of Multiprocessor Programming, is a recipient of the 2004 Gödel Prize in theoretical computer science and of the 2012 Dijkstra Prize in Distributed Computing, and is an ACM fellow. His recent interests include systems issues in machine learning and techniques for understanding how neural tissue computes by extracting connectivity maps of neural tissue, a field called connectomics.

The Software GPU: Making Inference Scale in the Real World(Talk)

Director of Data ScienceShopRunner

Ali Vanderveld, PhD

Ali Vanderveld is the Director of Data Science at ShopRunner, where her team leverages data from a network of over 100 retailers to build products for their 6 million members. Prior to ShopRunner, she was a staff data scientist at Civis Analytics, a consulting and software startup that helps companies, nonprofits, and political organizations better utilize their data. She has also worked at Groupon and as a technical mentor for the Data Science for Social Good Fellowship. Ali has a PhD in theoretical astrophysics from Cornell University and got her to start working as an academic researcher at Caltech, the NASA Jet Propulsion Laboratory, and the University of Chicago, working on the development teams for several space telescope missions, including ESA’s Euclid.

Using Computer Vision and NLP Together for Fashion Classification(Talk)

Co-creator of the Julia language, Co-founder & CTOJulia Computing, Inc.

Jeff Bezanson, PhD

Jeff is one of the creators of Julia, co-founding the project at MIT in 2009 and eventually receiving a Ph.D. related to the language in 2015. He continues to work on the compiler and system internals, while also working to expand Julia’s commercial reach as a co-founder of Julia Computing, Inc.

Advances in Julia for Data Science and ML(Workshop)

Author & Maintainer of PyTorch

Adam Paszke

Adam is an author and maintainer of PyTorch. He has worked with large organizations like Facebook AI Research, NVIDIA and Google, despite the fact that he has graduated from the Master’s program in Computer Science at the University of Warsaw only last year. Currently, he is also finishing his second major in Mathematics. His general interests include graph theory, programming languages, numerical computing and machine learning.

Step Up Your PyTorch with Custom Extension(Workshop)

Head of Data SciencePodium Education

Robert Alvarez, PhD

Robert loves to break deep technical concepts down to be as simple as possible, but no simpler.

Robert has data science experience in companies both large and small. He is currently Head of Data Science for Podium Education, where he builds models to improve student outcomes and an Adjunct Professor at Santa Clara University’s Leavey School of Business. Prior to Podium Education, he was a Senior Data Scientist at Metis teaching Data Science and Machine Learning. At Intel, he tackled problems in data center optimization using cluster analysis, enriched market sizing models by implementing sentiment analysis from social media feeds, and improved data-driven decision making in one of the top 5 global supply chains. At Tamr, he built models to unify large amounts of messy data across multiple silos for some of the largest corporations in the world. He earned a PhD in Applied Mathematics from Arizona State University where his research spanned image reconstruction, dynamical systems, mathematical epidemiology, and oncology.

Understanding the PyTorch Framework with Applications to Deep Learning(Training)

Supervisor, Data EducationChildren's Hospital of Philadelphia

Joy Payton

Joy Payton is a cloud engineer, data scientist, and adjunct professor who specializes in helping biomedical professionals conduct reproducible computational research. In addition to moving medicine forward through principles of open science and reproducibility, Joy also enjoys teaching citizen scientists how to use public data repositories to understand their own communities better and advocate for change from a data-centric perspective. Her various roles allow Joy to lead efforts to teach people how to write their first line of code and help anyone who’s interested climb the data science learning curve. Currently employed by the Children’s Hospital of Philadelphia and Yeshiva University, Joy is always open to hearing about open-source, data-centric volunteer opportunities for herself and her students.

Data Science and Machine Learning in the Cloud for Cloud Novices(Workshop)

Why Attend

Immerse yourself in talks, tutorials, and workshops on Machine Learning and Deep Learning tools, topics, models, and advanced trends

Expand your network and connect with like- minded attendees to discover how Machine Learning and Deep Learning knowledge can transform not only your data models but also your business and career

Meet and connect with the core contributors and top practitioners in the expanding and exciting fields of Machine Learning and Deep Learning

Learn how the rapid rise of intelligent machines is revolutionizing how we make sense of data in the real world and impacting the domains of business, society, healthcare, finance, manufacturing, and more

Previous Sessions

  • Machine Learning with R
  • Deep Learning with Tensorflow for Absolute Beginners
  • Deep Learning for Developers
  • Small to Big Data and Deep Learning
  • Leveraging Better User Experience through Chatbots
  • Guided Generative Adversarial Neural Network
  • Deep Learning in Keras
  • Distributed Deep Learning on Hops
  • Deep Learning with MXnet
  • Recommendation System Architecture and Algorithms

Who will attend

  • Top speakers and practitioners in Machine Learning and Deep Learning

  • Data Scientists and Data Analysts

  • Decision makers

  • Software Developers focused on Machine Learning and Deep Learning

  • Data Science Innovators

  • CEOs, CTOs, CIOs

  • Industry leaders

  • Core contributors in the fields of Machine Learning and Deep Learning

  • Data Science Enthusiasts

ODSC APAC Virtual Conference 2020 | December 8 - 9, 2020

Register Now
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