Schedule: Computer Vision sessions

The TensorFlow library provides for the use of dataflow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms. Robert Schroll offers an overview of the TensorFlow graph using its Python API. Read more.

BigDL is a powerful tool for leveraging Hadoop and Spark clusters for deep learning. Rich Ott offers an overview of BigDL’s capabilities through its Python interface, exploring BigDL's components and explaining how to use it to implement machine learning algorithms. You'll use your newfound knowledge to build algorithms that make predictions using real-world datasets. Read more.

Max Katz and Eric Levin walk you through the fundamentals of deep learning, from training neural networks to using results to improve performance and capabilities. You'll then apply your newfound knowledge to digital content creation and game development, as you create digital assets with deep learning. No prior deep learning experience is required. Read more.

High-resolution land cover maps help quantify long-term trends like deforestation and urbanization but are prohibitively costly and time intensive to produce. Mary Wahl and Banibrata De demonstrate how to use Microsoft’s Cognitive Toolkit and Azure cloud resources to produce land cover maps from aerial imagery by training a semantic segmentation DNN—both on single VMs and at scale on GPU clusters. Read more.

From social network photo filters to self-driving cars, computer vision has brought applied deep learning to the masses. Built by the pioneers of computer vision software, PyTorch enables developers to rapidly build computer vision models. Mo Patel and David Mueller offer an overview of computer vision fundamentals and walk you through using PyTorch to build computer vision applications. Read more.

Carl Osipov walks you through creating increasingly sophisticated image classification models using TensorFlow. Read more.

Xiaoyong Zhu, Gheorghe Iordanescu, Wilson Lee, and Ivan Tarapov walk you through building a deep learning model and intelligent applications on edge devices running iOS, Android, and Windows, using a working example that helps clinicians in areas with less access to radiologists identify possible lung diseases. Read more.

Transfer learning enables you to use pretrained deep neural networks and adapt them for various deep learning tasks (e.g., image classification, question answering, and more). Join Wee Hyong Tok and Danielle Dean to learn the secrets of transfer learning and discover how to customize these pretrained models for your own use cases. Read more.

Captricity has deployed a machine learning pipeline that can read handwriting at human-level accuracy. Ramesh Sridharan discusses the big ideas the company learned building and deploying this system, using data to identify specific problems to solve using AI and to evaluate and validate the algorithm itself and the overall system once deployed. Read more.

Facebook's strength in AI innovation comes from its ability to quickly bring cutting-edge research into large-scale production using a multifaceted toolset. Joseph Spisak explains how PyTorch 1.0 helps to accelerate the path from research to production by making AI development more seamless and interoperable. Read more.

Microsoft's AI for Earth team helps NGOs apply AI to challenges in conservation biology and environmental science. Jennifer Marsman outlines Microsoft’s objectives for AI for Earth and highlights recent successes in applying AI to agriculture, poacher detection, animal identification in camera trap and citizen scientist photography, and more. Read more.

Over 400M people worldwide have some sort of speech or hearing disorder that prevents them from participating in the job market. Goodman Gu offers an overview of Stride4All, an initiative using AI to open work up for disabled people and empower them for teamwork, and showcases a prototype that uses deep learning and computer vision technologies for gesture recognition of American Sign Language. Read more.

Labhesh Patel explains how deep learning is informing Jumio's computer vision through smarter data extraction, fraud detection, and risk scoring and how Jumio is leveraging massive datasets and human review to dramatically improve the accuracy of its machine learning algorithms to detect bogus IDs and streamline the verification process of legitimate documents. Read more.