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Vijay Reddy walks you through the process of building machine learning models with TensorFlow. You'll learn about data exploration, feature engineering, model creation, training, evaluation, deployment, and more. Read more.

Zachary Glassman leads a hands-on dive into building intelligent business applications using machine learning, walking you through all the steps of developing a machine learning pipeline. You'll explore data cleaning, feature engineering, model building and evaluation, and deployment and extend these models into two applications using real-world datasets. Read more.

Wenming Ye and Miro Enev offer an overview of deep learning along with hands-on Jupyter labs, demos, and instruction. You'll learn how DL is applied in modern business practice and how to leverage building blocks from the Amazon ML family of AI services. Read more.

Rachael Tatman shows you how to take an existing research project and make it fully reproducible using Kaggle Kernels. You'll learn best practices for and get hands-on experience with each of the three components necessary for completely reproducible research. Read more.

Chris Colbert, Ian Rose, and Saul Shanabrook walk you through using, extending, and developing custom components for JupyterLab using PhosphorJS, React, JavaScript, TypeScript, and CSS. You'll learn how to make full use of the power features of JupyterLab, customize it to your needs, and develop custom extensions, making complete use of JupyterLab's current capabilities. Read more.

Christopher Cho demonstrates how Kubernetes can be easily leveraged to build a complete deep learning pipeline, including data ingestion and aggregation, preprocessing, ML training, and serving with the mighty Kubernetes APIs. Read more.

Carol Willing, Min Ragan-Kelley, and Erik Sundell demonstrate how to provide easy access to Jupyter notebooks and JupyterLab without requiring users to install anything on their computers. You'll learn how to configure and deploy a cloud-based JupyterHub using Kubernetes and how to customize and extend it for your needs. Read more.

Jane Herriman uses Jupyter notebooks to show you why Julia is special, demonstrate how easy it is to learn, and get you writing your first Julia programs. Read more.

Networks, also known as graphs, are one of the most crucial data structures in our increasingly intertwined world. Social friendship networks, the web, financial systems, and infrastructure are all network structures. Noemi Derzsy explains how to generate, manipulate, analyze, and visualize graph structures that will help you gain insight about relationships between elements in your data. Read more.

April Clyburne-Sherin walks you through preparing Jupyter notebooks for computationally reproducible publication. You'll learn best practices for publishing notebooks and get hands-on experience preparing your own research for reuse, creating documentation, and submitting your notebook to share. Read more.

Kyle Kelley walks you through creating a new web application from the ground up, teaching you how to build on top of Jupyter's protocols in the process. Along the way, you'll learn about Jupyter's REST and streaming APIs, message spec, and the notebook format. Read more.

This two-part tutorial presents a sequence of advanced topics in Data Science, based on using Jupyter. Read more.

Rachael Tatman offers practical introduction to incorporating Jupyter notebooks into the classroom using active learning techniques. Read more.

JupyterLab—Jupyter's new frontend—goes beyond the classic Jupyter Notebook, providing a flexible and extensible web application with a set of reusable components. Jason Grout and Matthias Bussonnier walk you through using JupyterLab, explain how to transition from the classic Jupyter Notebook frontend to JupyterLab, and demonstrate JupyterLab's new powerful features. Read more.

In flipped learning, students encounter new material before class meetings, which helps them learn how to learn and frees up class time to focus on creative applications of the basic material. Lorena Barba and Robert Talbert discuss the use of Jupyter notebooks as a “tangible interface” for new material in a flipped course and share case studies from their own courses. Read more.

Sylvain Corlay, Johan Mabille, Wolf Vollprecht, and Martin Renou share the latest features of the C++ Jupyter kernel, including live help, auto-completion, rich MIME type rendering, and interactive widgets. Join in to explore one of the most feature-full implementations of the Jupyter kernel protocol that also brings Jupyter closer to the metal. Read more.

SWAN, CERN’s service for web-based analysis, leverages the power of Jupyter to provide the high energy physics community access to state-of-the-art infrastructure and services through a web service. Diogo Castro offers an overview of SWAN and explains how researchers and students are using it in their work. Read more.

The Globus service simplifies the utilization of large and distributed data on the Jupyter platform. Ian Foster explains how to use Globus and Jupyter to seamlessly access notebooks using existing institutional credentials, connect notebooks with data residing on disparate storage systems, and make data securely available to business partners and research collaborators. Read more.

Over the past two years, we have seen a dramatic shift in Jupyter’s deployment, from ad hoc usage by individuals to production enterprise application at scale. Brian Granger explains how this has expanded the Jupyter community and revealed new use cases with new challenges and opportunities. Read more.

Luciano Resende outlines a pattern for building deep learning models using the Jupyter Notebook's interactive development in commodity hardware and leveraging platforms and services such as Fabric for Deep Learning (FfDL) for cost-effective full dataset training of deep learning models. Read more.

The Data Science Modules program at UC Berkeley creates short explorations into data science using notebooks to allow students to work hands-on with a dataset relevant to their course. Mariah Rogers, Ronald Walker, and Julian Kudszus explain the logistics behind such a program and the indispensable features of JupyterHub that enable such a unique learning experience. Read more.

Running infrastructure is challenging for an open source community. Yuvi Panda shares lessons drawn from the small community that operates MyBinder.org, covering the social and technical processes for keeping MyBinder.org reliable in the most open, transparent, and inclusive way possible, using pretty graphs about the state of MyBinder.org that anyone can see in real time. Read more.

LSST is an ambitious project to map the sky in the fastest, widest, and deepest survey ever made. The project's database disrupts traditional astronomical workflows, and its science platform requires a paradigm shift in how astronomy is done. Adam Thornton discusses the challenges of providing production services on a notebook-based architecture and the compelling advantages of JupyterLab. Read more.

In silico prediction of chemical properties has seen vast improvements in both veracity and volume of data but is currently hamstrung by a lack of transparent, reproducible workflows coupled with environments for visualization and analysis. Chris Harris offers an overview of a platform that uses Jupyter notebooks to enable an end-to-end workflow from simulation setup to visualizing the results. Read more.

In Capital One's recent exploration of "notebook" offerings, JupyterHub emerged as a top contender that could serve as a potential platform for analytics even in highly regulated industries like financial services. David Schaaf and Shivraj Ramanan discuss Capital One's journey and explain how Jupyter has become a part of the company's ever-growing analytics toolkit. Read more.

Kevin McCormick explains the story of two approaches which were used internally at AWS to accelerate new ML algorithm development, and easily package Jupyter notebooks for scheduled execution, by creating custom Jupyter kernels that automatically create Docker containers, and dispatch them to either a distributed training service or job execution environment. Read more.

In an effort to broaden graduates' mathematical toolkit and address gender equity in STEM education, Rob Newton has led the implementation of Python projects across his school's entire ninth-grade math courses. Now every student in the ninth grade completes three python projects that introduce programming and integrate them with the ideas developed in class. Read more.

Using an nteract project, papermill, Matthew Seal walks you through how Netflix uses notebooks to track user jobs and make a simple interface for work submission. You’ll get an inside peek at how Netflix is tackling the scheduling problem for a range of users who want easily managed workflows. Read more.

Climate science is being flooded with petabytes of data, overwhelming traditional modes of data analysis. The Pangeo project is building a platform to take big data climate science into the cloud using SciPy and large-scale interactive computing tools. Join Ryan Abernathey and Yuvi Panda to find out what the Pangeo team is building and why and learn how to use it. Read more.

Notebook authors often consider only the interactive experience of creating computable documents. However, the dynamic state of a notebook is a minor period in its lifecycle; the majority is spent as a file at rest. Tony Fast and Nick Bollweg explore conventions that create notebooks with value long past their inception as documents, software packages, test suites, and interactive applications. Read more.

Matt Greenwood explains why Two Sigma, a company in a space notorious for protecting IP, thinks it's important to contribute to the open source community. Matt covers the evolution of Two Sigma's thinking and policies over the past five years and makes a case for why other companies should make a commitment to the open source ecosystem. Read more.

Chakri Cherukuri offers an overview of the interactive widget ecosystem available in the Jupyter notebook and illustrates how Jupyter widgets can be used to build rich visualizations of machine learning models. Along the way, Chakri walks you through algorithms like regression, clustering, and optimization and shares a wizard for building and training deep learning models with diagnostic plots. Read more.

Join this panel of seasoned educators and the cochairs of the education track at JupyterCon to look to the future of Jupyter in teaching and learning. Read more.

JupyterHub is a multiuser server for Jupyter notebooks, focused on supporting deployments in research and education. Min Ragan-Kelley, Carol Willing, and Yuvi Panda discuss recent additions and future plans for the project. Read more.

William Stein explains how CoCalc relates to Project Jupyter and shares how he implemented real-time collaborative editing of Jupyter notebooks in CoCalc. Read more.

Julia and Jupyter share a common evolution path: Julia is the language for modern technical computing, while Jupyter is the development and presentation environment of choice for modern technical computing. Viral Shah and Jane Herriman discuss Julia's journey and the impact of Jupyter on Julia's growth. Read more.

Dave Stuart explains how Jupyter was used inside the US Department of Defense and the greater intelligence community to empower thousands of "citizen data scientists" to build and share analytics in order to meet the community’s dynamic challenges. Read more.

Netflix relies on notebooks to inform decisions and fuel experiments across the company. Now Netflix wants to go even further to deliver a compelling notebook experience for end-to-end workflows. Michelle Ufford shares some of the big bets Netflix is making on notebook infrastructure, covering data use at Netflix, architecture, kernels, UIs, and open source projects, such as nteract. Read more.

Students learn by doing. Carol Willing, Jessica Forde, and Erik Sundell demonstrate the value of interactive content, using Jupyter notebooks, widgets, and visualization libraries, share notable examples of projects within the Jupyter community, and outline ways educators can help students develop data science literacy and use computational skills to build upon their interests. Read more.

Stephanie Stattel and Paul Ivanov walk you through a series of extensions that demonstrate the power and flexibility of JupyterLab’s architecture, from targeted functionality modifications to more extreme atmospheric changes that require extensive decoupling and flexibility within JupyterLab. Read more.

Making Jupyter accessible to all members of a research organization, regardless of their programming ability, empowers it to best utilize the latest analysis methods while avoiding bottlenecks. Thorin Tabor offers an overview of the GenePattern Notebook, which offers a wide suite of enhancements to the Jupyter environment to help bridge the gap between programmers and nonprogrammers. Read more.

Nicolas Fernandez offers an overview of Clustergrammer-Widget, an interactive heatmap Jupyter widget that enables users to easily explore high-dimensional data within a Jupyter notebook and share their interactive visualizations using nbviewer. Read more.

Government agencies have found it difficult to serve taxpayers because of the technical, bureaucratic, and ethical issues associated with access and use of sensitive data. Julia Lane explains how the Coleridge Initiative has partnered with Jupyter to design ways that can address the core problems such organizations face. Read more.

The Jupyter in education track concludes with breakout sessions that allow presenters and attendees alike to work together on specific topics, potentially leading to new projects and collaborations. Read more.

Jupyter displays a rich array of media types out of the box. M Pacer explains how to use these capabilities to their full potential, covering how to add rich displays to existing and new Python classes and how to customize the way notebooks are converted to other formats. These skills will enable anyone to make beautiful objects with Jupyter. Read more.

Bo Peng offers an overview of Script of Scripts (SoS), a Python 3-based workflow engine with a Jupyter frontend that allows the use of multiple kernels in one notebook. This unique combination enables users to analyze data using multiple scripting languages in one notebook and, if needed, convert scripts to workflows in situ to analyze large amounts of data on remote systems. Read more.

The Binder project drastically lowers the bar to sharing and reusing software. Users wanting to try out someone else’s work need only click a single link to do so. Tim Head offers an overview of the Binder project and explores the concepts and ideas behind it. Tim then showcases examples from the community to show off the power of Binder. Read more.

Join in for the Business Summit's roundtable discussion with participation from IBM, Capital One, the DoD, AWS, Oracle, and others. Speakers will discuss important issues in our current environment—everything from compliance and GDPR to ML models. Read more.

Laura Noren offers an overview of a research project on the various infrastructure models supporting data science in research settings in terms of funding, educational uses, and research utilization. Laura then shares some of the findings, comparing the national federation model currently established in Canada to the more grassroots efforts in many US universities. Read more.

Reproducible data is essential for notebooks that work across time, across contributors, and across machines. Jackson Brown and Aneesh Karve demonstrate how to use an open source data registry to create reproducible data dependencies for Jupyter and share a case study in open science over terabyte-size image datasets. Read more.

Kerim Kalafala and Nicholai L'Esperance share their experiences using Jupyter notebooks as a critical aid in designing the next generation of IBM Power and Z processors, focusing on analytics on graphs consisting of hundreds of millions of nodes. Along the way, Kerim and Nicholai explain how they leverage Jupyter notebooks as part of their overall design system. Read more.

Sandra Savchenko-de Jong offers an overview of Renku, a highly scalable and secure open software platform designed to make (data) science reproducible, foster collaboration between scientists, and share resources in a federated environment. Read more.

In the corporate tax world, Microsoft Excel—the king of spreadsheets—is the default tool for tracking information and managing tasks, but tax professionals are often annoyed by slowly updating or broken linked or referenced cells within or between spreadsheets. Jinli Ma explains how the Jupyter Notebook does a better job than Microsoft Excel with the original issued discount calculation process. Read more.

Over the past 18 months, Ian Allison and James Colliander have deployed Jupyter to more than 8,000 users at universities across Canada. Ian and James offer an overview of the Syzygy platform and explain how they plan to scale and deliver the service nationally and how they intend to make Jupyter integral to the working experience of students, researchers, and faculty members. Read more.

Project Jupyter aims to provide a consistent set of tools for data science workflows, from the exploratory phase of the analysis to the sharing of the results. Maarten Breddels and Sylvain Corlay offer an overview of Jupyter's interactive widgets framework, which enables rich user interaction, including 2D and 3D interactive plotting, geographic data visualization, and much more. Read more.

Massive collections of data on the Earth's changing environment, collected by satellite sensors and generated by Earth system models, are being exposed via web APIs by multiple providers. Tyler Erickson highlights the use of JupyterLab and Jupyter widgets in analyzing complex high-dimensional datasets, providing insights into how our Earth is changing and what the future might look like. Read more.

I have been using and teaching Python for many years. I wrote a best-selling book about learning data science. And here's my confession: I don't like notebooks. (There are dozens of us!) I'll explain why I find notebooks difficult, show how they frustrate my preferred pedagogy, demonstrate how I prefer to work, and discuss what Jupyter could do to win me over. Read more.

Many US government agencies are just getting started with machine learning. As a result, data scientists need to de-"black box" models as much as possible. One simple way to do this is to transparently show how the model is coded and its results at each step. Notebooks do just this. Catherine Ordun walks you through a notebook built for RNNs and explains how government agencies can use notebooks. Read more.

For the last four years, Douglas Blank has used nothing but Jupyter in the classroom—from a first-year writing course to a course on assembly language, from biology to computer science, from lectures to homework. Join in to learn how Douglas has leveraged Jupyter and discover the successes and failures he experienced along the way. Nicole Petrozzo then offers a student's perspective. Read more.

Many of us believe that gender diversity in open source projects is important. (If you don’t, this isn’t going to convince you.) But what things are correlated with improved gender diversity, and what can we learn from similar historic industries? Holden Karau and Matt Hunt explore the diversity of different projects, examine historic EEOC complaints, and detail parallels and historic solutions. Read more.

Creating flood maps for coastal and riverine communities requires geospatial processing, statistical analysis, finite element modeling, and a team of specialists working together. Seth Lawler explains how using the feature-rich JupyterLab to develop tools, share code with team members, and document workflows used in the creation of flood maps improves productivity and reproducibility. Read more.

Dataflow notebooks build on the Jupyter Notebook environment by adding constructs to make dependencies between cells explicit and clear. David Koop offers an overview of the Dataflow kernel, shows how it can be used to robustly link cells as a notebook is developed, and demonstrates how that notebook can be reused and extended without impacting its reproducibility. Read more.

The key to successful threat detection in cybersecurity is fast response. George Williams, Harini Kannan, and Alex Comerford offer an overview of specialized extensions they have built for data scientists working in cybersecurity that can be used and deployed via JupyterHub. Read more.

As practitioners of open science begin to migrate their educational material into pubic repositories, many of their common practices and platforms can be used to streamline the instruction material development process. Elizabeth Wickes explains how open science practices can be used in an educational context and why they are best facilitated by tools like the Jupyter Notebook. Read more.

Jupyter's straightforward, out-of-the-box experience has been important for its success in widespread adoption. But good defaults only go so far. Join Afshin Darian, M Pacer, Min Ragan-Kelley, and Matthias Bussonnier to go beyond the defaults and make Jupyter your own. Read more.

MapD Core is an open source analytical SQL engine that has been designed from the ground up to harness the parallelism inherent in GPUs. This enables queries on billions of rows of data in milliseconds. Randy Zwitch offers an overview of the MapD kernel extension for the Jupyter Notebook and explains how to use it in a typical machine learning workflow. Read more.

Even with good intentions, analysis notebooks can quickly accumulate a mess of false starts and out-of-order statements. Best practices encourage cleaning up a notebook to ensure reproducibility, but many analyses will never reach this cleaned-up state. Kevin Zielnicki offers an overview of Nodebook, a Jupyter plugin that encourages reproducibility by preventing inconsistency. Read more.

Gerald Rouselle reviews some of the trends in modern data and analytics ecosystems for large enterprises and shares some of the key challenges and opportunities for Jupyter adoption. He also details some recent examples and experiments in incorporating Jupyter in commercial products and platforms. Read more.

The nbinteract package converts Jupyter notebooks with widgets into interactive, standalone HTML pages. Its built-in support for function-driven plotting makes authoring interactive pages simpler by allowing users to focus on data, not callbacks. Sam Lau and Caleb Siu offer an overview of nbinteract and walk you through the steps to publish an interactive web page from a Jupyter notebook. Read more.

Ian Rose and Chris Colbert walk you through the JupyterLab interface and codebase and explain how it fits within the overall roadmap of Project Jupyter. Read more.

Satellite imagery can be a critical resource during disasters and humanitarian crises. While the community has improved data sharing, we still struggle to create reusable data science to solve problems on the ground. Sean Gorman offers an overview of GBDX Notebooks, a step toward creating an open data science community built around Jupyter to stream imagery and share analysis at scale. Read more.

JupyterLab and Plotly both provide a rich set of tools for working with data. When combined, they create a powerful computational environment that enables users to produce versatile, robust visualizations in a fast-paced setting. Lindsay Richman demonstrates how to use JupyterLab, Plotly, and Plotly's Python-based Dash framework to create dynamic charts and interactive reports. Read more.

Hundreds of PayPal's data scientists, analysts, and developers use Jupyter to access data spread across filesystem, relational, document, and key-value stores, enabling complex analytics and an easy way to build, train, and deploy machine learning models. Romit Mehta and Praveen Kanamarlapudi explain how PayPal built its Jupyter infrastructure and powerful extensions. Read more.

RISE has evolved into the main slideshow machinery for live presentations within the Jupyter notebook. Damián Avila explains how to install and use RISE. You'll also discover how to customize it and see some of its new capabilities. Damián concludes by discussing the migration from RISE into a new JupyterLab-RISE extension providing RISE-based capabilities in the new JupyterLab interface. Read more.

John Miller offers an overview of the Emacs IPython Notebook (EIN), a full-featured client for the Jupyter Notebook in Emacs, and shares a brief history of its development. Read more.

Joshua Patterson, Leo Meyerovich, and Keith Kraus demonstrate how to use PyGDF and other GoAi technologies to easily analyze and interactively visualize large datasets from standard Jupyter notebooks. Read more.

Scott Sanderson explores how interactivity can and should influence the design of software libraries, details how the needs of interactive users differ from the needs of application developers, and shares techniques for improving the usability of libraries in interactive environments without sacrificing robustness in noninteractive environments. Read more.

The Business Summit concludes with "unconference"-style breakout sessions that allow enterprise stakeholders to give input to Project Jupyter directly. Read more.