You are viewing content from a past/completed QCon

Workshop: [SOLD OUT] (Deep) Learn You a Neural Net For Great Good!

Location: Seacliff CD

Duration: 1:00pm - 4:00pm

Day of week: Thursday

Level: Beginner

Key Takeaways

  • Neural Nets are quite similar to other, simpler, methods, and can be written from scratch without deep mathematical knowledge!

  • NNs are extremely composable, making it easy to mix and match modules to create more complex architectures.

  • You (yes you!) can build your own NN from scratch using the tools you already know.


  • Participants should have experience using python interactively (either in a notebook environment or via a terminal such as IPython/BPython)
  • Participants should have a working Python 3 installation on their laptop, with Numpy and Scikit-learn installed.

You (yes you!) can build your own NN from scratch using the tools you already know.

Neural networks and deep learning are fundamental to modern machine learning, yet often appear scarier than they really are. Many users of Scikit-learn et al. can apply ML techniques (perhaps including deep learning) through these tools, but do not always grok fully what happens beneath the surface. Other more engineering-oriented practitioners are put off entirely by the seeming complexity of DL.

We walk through a live coding practicum (in a Jupyter Notebook) in which we implement a feed-forward, fully-connected neural net in numpy, initially training it via a for-loop to demonstrate core concepts, and finally codifying the NN as a Scikit-learn style classifier with which one can fit & predict on one’s own data. We make iterative improvements to code quality along the way, and reach a level of abstraction suitable for reusable, modular machine learning.

The focus of this talk is on the practicum of implementing one’s own NN algorithm, though we also review the most important mathematical and theoretical components of NNs to ground the practicum for attendees. Mathematical review touches on the nature of gradients, what they are, how they relate to derivates, and how backpropagation works at a high level. Attendees will leave the talk with a better understanding of deep learning through iterative optimization.

Speaker: Michael Stewart

Machine Learning Engineer @Opendoor

Stu (Michael Stewart) is a machine learning engineer at Opendoor in San Francisco. Previously, he was a data scientist and engineer at Uber, and an economic researcher at the Federal Reserve Bank of New York. He is a National Science Foundation Graduate Research Fellowship Awardee in Economic Sciences.

Find Michael Stewart at

Proposed Tracks

  • Architectures You've Always Wondered About

    Next-gen architectures from the most admired companies in software, such as Netflix, Google, Facebook, Twitter, & more

  • Machine Learning without a PhD

    AI/ML is more approachable than ever. Discover how deep learning and ML is being used in practice. Topics include: TensorFlow, TPUs, Keras, PyTorch & more. No PhD required.

  • Production Readiness: Building Resilient Systems

    Making systems resilient involves people and tech. Learn about strategies being used from chaos testing to distributed systems clustering.

  • Building Predictive Data Pipelines

    From personalized news feeds to engaging experiences that forecast demand: learn how innovators are building predictive systems in modern application development.

  • Modern Languages: The Right Language for the Job

    We're polyglot developers. Learn languages that excel at very specific tasks and remove undifferentiated heavy lifting at the language level.

  • Delivering on the Promise of Containers

    Runtime containers, libraries and services that power microservices.

  • Evolving Java & the JVM

    6 month cadence, cloud-native deployments, scale, Graal, Kotlin, and beyond. Learn how the role of Java and the JVM is evolving.

  • Trust, Safety & Security

    Privacy, confidentiality, safety and security: learning from the frontlines.

  • Beyond the Web: What’s Next for JavaScript

    JavaScript is the language of the web. Latest practices for JavaScript development in and out of the browser topics: react, serverless, npm, performance, & less traditional interfaces.

  • Modern Operating Systems

    Applied, practical & real-world deep-dive into industry adoption of OS, containers and virtualization, including Linux on.

  • Optimizing You: Human Skills for Individuals

    Better teams start with a better self. Learn practical skills for IC.

  • Modern CS in the Real World

    Thoughts pushing software forward, including consensus, CRDT's, formal methods & probabilistic programming.

  • Human Systems: Hacking the Org

    Power of leadership, Engineering Metrics and strategies for shaping the org for velocity.

  • Building High-Performing Teams

    Building, maintaining, and growing a team balanced for skills and aptitudes. Constraint theory, systems thinking, lean, hiring/firing and performance improvement

  • Software Defined Infrastructure: Kubernetes, Service Meshes & Beyond

    Deploying, scaling and managing your services is undifferentiated heavy lifting. Hear stories, learn techniques and dive deep into what it means to code your infrastructure.

  • Practices of DevOps & Lean Thinking

    Practical approaches using DevOps and a lean approach to delivering software.

  • Operationalizing Microservices: Design, Deliver, Operate

    What's the last mile for deploying your service? Learn techniques from the world's most innovative shops on managing and operating Microservices at scale.

  • Developer Experience: Level up your Engineering Effectiveness

    Improving the end to end developer experience - design, dev, test, deploy and operate/understand.