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Michael Li and Russ Martin offer a nontechnical overview of AI and data science. You’ll learn common techniques and how to apply them as well as common pitfalls to avoid. Along the way, you’ll pick up the language of AI and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. Dylan Bargteil walks you through TensorFlow's capabilities in Python, teaching you how to build machine learning algorithms piece by piece and use the Keras API provided by TensorFlow with several hands-on applications.

PyTorch is a machine learning library for Python that allows users to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Ana Hocevar introduces the PyTorch workflow and demonstrates how to use it to build deep learning models using real-world datasets.

Francesca Lazzeri, Wee Hyong Tok, and Krishna Anumalasetty walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources.

Machine learning (ML) and deep learning (DL) projects are becoming increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye and Miro Enev give you a practical introduction to the next step in DL learning, with lecture, demos, and hands-on labs.

Delip Rao and Brian McMahan explore 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.

Learn how to deploy enterprise AI solutions with Intel and its partner ecosystem. This event features offerings for banking and financial services, retail, and healthcare as well as cross-industry AI solutions.

Mo Patel leads a deep dive into all aspects of the PyTorch lifecycle via hands-on examples such as image classification, text classification, and linear modeling. Along the way, you'll explore other aspects of machine learning such as transfer learning, data modeling, and deploying to production with immersive labs.

Alex Siegman and Kabir Seth walk you through the steps necessary to appropriately leverage AI in a large organization. This includes ways to identify business opportunities that lend themselves to AI as well as best practices on everything from data intake and manipulation to model selection, output analysis, development, and deployment, all while navigating a complex organizational structure.

Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making.

Gunnar Carlsson explains how to use topological data analysis to describe the functioning and learning of a neural network in a compact and understandable way—resulting in material speedups in performance (training time and accuracy) and enabling data-type customization of neural network architectures to further boost performance and widen the applicability of the method to all datasets.

Justina Petraityte offers a hands-on walk-through of developing intelligent AI assistants based entirely on machine learning and using only the open source tools Rasa NLU and Rasa Core. You'll learn the fundamentals of conversational AI and best practices for developing AI assistants that scale and learn from real conversational data.

Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include word2vec, recurrent neural networks and variants (LSTM, GRU), and convolutional neural networks.

Time series are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations. Join Bruno Gonçalves to learn how to use recurrent neural networks to model and forecast time series and discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches.

Purpose, a well-defined problem, and trust from people are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment.

Ignite is happening at AI in New York on Tuesday, April 16. Join us for a fun, high-energy evening of five-minute talks—all aspiring to live up to the Ignite motto: Enlighten us, but make it quick.

Rachel Bellamy, Kush Varshney, Karthikeyan Natesan Ramamurthy, and Michael Hind explain how to use and contribute to AI Fairness 360—a comprehensive Python toolkit that provides metrics to check for unwanted bias in datasets and machine learning models and state-of-the-art algorithms to mitigate such bias.

Ray is a general purpose framework for programming your cluster. Robert Nishihara, Philipp Moritz, Ion Stoica, and Eric Liang lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms.

Get to know your fellow attendees over dinner. We've made reservations for you at some of the most sought-after restaurants in town. This is a great chance to make new connections and sample some of the great cuisine New York City has to offer.