Models and Methods

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

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.

David Arpin offers an overview of the Amazon SageMaker machine learning platform, walking you through setup and using Amazon SageMaker Notebook (a hosted Jupyter Notebook server). You'll get hands-on experience with SageMaker's built-in deep learning algorithm as you dive into building your own neural network architecture using SageMaker's prebuilt TensorFlow containers. 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.

Uber has implemented an ML and NLP system that suggests the most likely solutions to a ticket to its customer support representatives, making them faster and more accurate while providing a better user experience. Piero Molino, Huaixiu Zheng, and Yi-Chia Wang describe how Uber built the system with traditional and deep learning models and share the lessons learned along the way. Read more.

Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially for computer vision. Anirudh Koul explains how to bring the power of convolutional neural networks and deep learning to memory- and power-constrained devices like smartphones. Read more.

KC Tung explains why LSTM provides great flexibility to model the consumer touchpoint sequence problem in a way that allows just-in-time insights about an advertising campaign's effectiveness across all touchpoints (channels), empowering advertisers to evaluate, adjust, or reallocate resources or investments in order to maximize campaign effectiveness. Read more.

In spite of the enormous excitement about the potential of deep learning, several key challenges—from prohibitive hardware requirements to immature software offerings—are impeding its widespread enterprise adoption. Evan Sparks and Ameet Talwalkar detail fundamental challenges facing organizations looking to adopt deep learning and present novel solutions to overcome several of them. Read more.

Modern radiological lung cancer screening is an entirely manual process, leading to high costs and inter-reader variability. Daniel Golden offers an overview of a deep learning-based system that automatically detects and segments lung nodules in lung CT exams and explains how it was tested for safety and efficacy. The system is FDA cleared and segments nodules as accurately as a clinician. Read more.

Earlier this year, Amazon, Facebook, and Microsoft partnered to create the Open Neural Network Exchange (ONNX)—an open format to represent deep learning models. Sarah Bird explains in detail how the ONNX framework can help you take AI from research to reality as quickly as possible. Read more.

Join Ari Kaplan, Erin LeDell, Chris Coad, and David Kearns to see where AI meets business intelligence, as they explore the latest ML technologies and concepts powering today's decisions, including Hortonworks, Aginity Amp, H2O.ai, IBM Data Science Experience, and more—using real-life baseball data to illustrate the concepts. Read more.

Personalization is a common theme in social networks and ecommerce businesses. Personalization at Uber involves an understanding of how each driver and rider is expected to behave on the platform. Ankit Jain explains how Uber employs deep learning using LSTMs and its huge database to understand and predict the behavior of each and every user on the platform. Read more.

Online fraud is often orchestrated by organized crime rings, who use malicious user accounts to actively target modern online services for financial gain. Ting-Fang Yen shares a real-time, scalable fraud detection solution backed by deep learning and built on Spark and TensorFlow and demonstrates how the system outperforms traditional solutions such as blacklists and machine learning. Read more.

Machine learning algorithms are being developed and improved at an incredible rate but are not necessarily accessible to the broader community. A. Besir Kurtulmus offers an overview of DanKu, a new blockchain-based protocol for evaluating and purchasing ML models on a public blockchain such as Ethereum that provides everyone access to high-quality, objectively measured machine learning models. Read more.

Blockchain technologies offer new internet primitives for creating open and online data marketplaces. Roger Chen explores how data markets can be constructed and how they offer a shared resource on the internet for AI-based research, discovery, and development. Read more.

Abhishek Tayal offers insight into how Twitter's ML platform team, Cortex, is developing models, related tooling, and infrastructure with the objective of making entity embeddings a first-class citizen within Twitter's ML platform. Abhishek also shares success stories on how developing such an ecosystem increases efficiency and productivity and leads to better outcomes across product ML teams. 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.

Predicting driver intention and behavior is of great importance for the planning and decision-making processes of autonomous driving vehicles. Zhou Xing shares a methodology that can be used to build and train a predictive driver system, helping to learn on-road drivers' intentions, behaviors, associated risks, etc. Read more.

Lydia Liu discusses the results of research on how static fairness criteria interact with temporal indicators of well-being. These results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria and suggest a range of new challenges and trade-offs. Read more.