Schedule: Deep Learning tools sessions

TensorFlow is an increasingly popular tool for deep learning. Robert Schroll offers an overview of the TensorFlow graph using its Python API. You'll start with simple machine learning algorithms and move on to implementing neural networks. Along the way, Robert covers several real-world deep learning applications, including machine vision, text processing, and generative networks. Read more.

Delip Rao explores natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks. Read more.

Join Denis Batalov for an overview of the Amazon SageMaker machine learning platform. Denis walks you through setting up an Amazon SageMaker notebook (a hosted Jupyter Notebook server), using a built-in SageMaker deep learning algorithm, and building your own neural network architecture using SageMaker's prebuilt TensorFlow containers. Read more.

Computer vision has led the artificial intelligence renaissance, and pushing it further forward is PyTorch, a flexible framework for training models. Mo Patel offers an overview of computer vision fundamentals and walks you through PyTorch code explanations for notable objection classification and object detection models. Read more.

Benoit Dherin explains how machine learning is applied to image classification, discusses evolving methods and challenges, and walks you through creating increasingly sophisticated image classification models using TensorFlow. Read more.

Jonathan Hung, Keqiu Hu, and Anthony Hsu offer an overview of TensorFlow on YARN (TonY), a framework to natively run TensorFlow on Hadoop. TonY enables running TensorFlow distributed training as a new type of Hadoop application. Its native Hadoop connector, together with other features, aims to run TensorFlow jobs as reliably and flexibly as other first-class objects on Hadoop. Read more.

Machine learning sits at the core of many essential products and services at Facebook. Yangqing Jia and Dmytro Dzhulgakov offer an overview of the hardware and software infrastructure that supports machine learning at global scale. Read more.

Dmytro Dzhulgakov explores PyTorch 1.0, from its start as a popular deep learning framework for flexible research to its evolution into an end-to-end platform for building and deploying AI models at production scale. Read more.

Gaurav Kaul, Grigori Fursin, and Suneel Marthi share trade-offs and design choices that are applicable to deep learning models when training in the cloud, specifically focusing on convergence and numerical stability, which are very important for autonomous driving and medical imaging. They then demonstrate how to optimize cost, performance, and convergence using CPU spot instances in AWS. Read more.

Machine learning and object recognition have matured to the point that exciting applications are now possible. Anmol Jagetia demonstrates how to create a Pokédex that uses a camera phone to recognize the Pokémon it's looking at in real time. You'll see how to gather data, prepare your dataset, tune models, and deploy it to a mobile device, using the same tech that is used in self-driving cars. Read more.

Dramatic progress has been made in computer vision. Deep neural networks (DNNs) trained on millions of images can recognize thousands of different objects, and they can be customized to new use cases. Vanja Paunic and Patrick Buehler outline simple methods and tools that enable users to easily and quickly adapt Microsoft's state-of-the-art DNNs for use in their own computer vision solutions. Read more.