Deep Learning Talks Coming to the ODSC Virtual Conference April 14–17
Deep Learning (with TensorFlow 2): Dr. Jon Krohn | Untapt
This deep learning primer brings the revolutionary machine-learning approach behind contemporary artificial intelligence to life with interactive demos featuring TensorFlow 2, the major, cutting-edge revision of the world’s most popular Deep Learning library.
The Software GPU: Making Inference Scale in the Real World: Nir Shavit, PhD | Neural Magic, MIT
This session will show how to run deep neural networks at scale with GPU-class performance on commodity CPUs, with all of the deployment flexibility of a software solution. Data science teams can run models at exceptional speeds, without the expense and complexity of dedicated hardware.
Modern and Old Reinforcement Learning: Leonardo De Marchi | Badoo/MagicLab
In this workshop we will explore Reinforcement Learning, starting from its fundamentals and ending creating our own algorithms.
Understanding the PyTorch Framework with Applications to Deep Learning: Robert Alvarez, PhD | Podium Education
In this session, we will cover how to create Deep Neural Networks using the PyTorch framework on a variety of examples. The material will range from beginner — understanding what is going on “”under the hood””, coding the layers of our networks, and implementing backpropagation — to more advanced material on RNNs, CNNs, LSTMs, & GANs.
From Research to Production: Performant Cross-platform ML/DNN Model Inferencing on Cloud and Edge with ONNX Runtime: Faith Xu & Prabhat Roy | Microsoft
In this workshop, we will demonstrate the versatility and power of ONNX and ONNX Runtime by converting a traditional ML scikit-learnpipeline to ONNX, followed by exporting a PyTorch-trained Deep Neural Network model to ONNX. These models will then be deployed to Azure as a cloud service using Azure Machine Learning services, and to Windows or Mac devices for on-device inferencing.
Uncertainty in Deep Learning: Rebecca Russell, PhD | Draper
This talk will survey methods for quantifying and handling different sources of uncertainty, with a focus on practical, scalable techniques for a variety of common use cases. From principled Bayesian approaches to post-hoc calibration, we’ll cover the theory, tools, techniques, and tips you need to better handle uncertainty in your own deep learning applications.
Applied Deep Learning: Building a Chess Object Detection Model with TensorFlow: Joseph Nelson | Roboflow.ai
In this tutorial, we will introduce how to build an object detection model. Specifically, we will build an object detection model that identifies chess pieces (a custom dataset provided by the presenter). In doing so, participants will gain insight into the fundamentals of computer vision.
Deploying Deep Learning Models as Microservices: Saishruthi Swaminathan | IBM
Powering your application with deep learning is no walk in the park, but is certainly attainable with some tricks and good practice. We will kick off with an overview of how deep learning models are best published as Docker images on DockerHub.
Using Computer Vision and NLP Together for Fashion Classification: Ali Vandervelt, PhD | ShopRunner
In this talk, Ali will motivate the benefits of combining image and text data for classification problems, and introduce her newly open-sourced library for building multi-task computer vision and natural language processing deep learning pipelines using PyTorch.
Hybrid Deep Learning Approach to Speed up Certain Numerical Simulations: Cheng Zhan, PhD | Microsoft
In this presentation, we will demonstrate how to leverage deep learning to speed up production forecasting, as well as seismic imaging.
This list doesn’t cover everything. Be sure to check out all of the ODSC East 2020 Virtual Conference speakers here and filter by the category of your choice.
