Here are some abstracts/summarys of talks I've made or given
React is a great framework for declarative logic that propagates data from one DOM element to another. Canvas is great at changing state imperatively in a compute efficient way for graphics rendering.
What's not so great is trying to use them together.
The clash of architectures can make your codebase messy and hard to debug.
Fortunately, we can use the Component pattern of React to isolate imperative code and actually make the graphics more performant. Attendees will learn how to properly structure their project to leverage React for organizing imperative Canvas logic for cleaner code and better performing apps.
Skin Cancer is the most common Cancer in the world with 5.4 million new cases every year in the United States alone. When diagnosed early, melanoma is easily cured by simple outpatient surgical excision. Using the newest techniques with Keras and Python, this talk will go over how we can detect Melanoma from a large dataset of patient skin lesions. Attendees will leave with the knowledge of using a labeled dataset to generate a Deep Learning model for a camera app.
Excited about all the advancements in AI and Machine Learning? Do you like dogs? What better way to learn a new disapline by making a dog breed detection app? In this session, we'll be learning the basics of Machine Learning, how Convolutional Neural Networks can help with Computer Vision problems, how to use Transfer Learning can speed up our training process, and how we can use our new model in a camera web app.
You've seen the power of Machine Learning, and you've done the Hello World projects. So now how do you convince your boss at work it should be apart of the organization? Or even better, how do you convince your boss it should be your new full time job? This talk will discuss business problems ML can be great at solving, how to pitch to stakeholders, then how to structure the project pipeline. Attendees will leave with knowledge of the practicality of using ML at work, frame the business case, and structure the project pipeline.
If there's one area in tech that's made huge growth in 2017, it's Machine Learning. Instead of using explicit code to solve problems, ML aims to let the data solve them for you. With all this hype, do we really understand what's going on under the hood? This hands-on session will give attendees the tools and concepts needed to write your very own Neural Networks! Topics that will be covered include Matrices, Neurons, Gradient Descent, Loss Functions, Activation Functions, and Back Propagation. Attendees will leave with the ability to not just build their own Machine Learning models, but also dig deeper into the data and customize their approach per project.
With all the hype right now around Machine Learning and AI, it's easy to become confused on how and where to get started. Do you focus on the math, or the frameworks, or the problems? This session will teach attendees what Machine Learning is (and what it's not) and how to get started with TensorFlow/Keras. Attendees will leave having seen a practical example of how to predict handwritten numbers and the knowledge that Machine Learning is approachable and fun.
So you've learned what Machine Learning is, you've done the Hello World projects, now how do you get your boss to let you use it at work? This talk will cover some example business problems that Machine Learning can be great at solving, how to pitch it to your boss, then how one would go about starting a project with production in mind. Attendees will leave with knowledge of the practicality of using Machine Learning at work, how to go about convincing stakeholders, and how to design and structure your Machine Learning project.
Once you've developed a kickass Machine Learning model, you need a way to get that model to your computing devices (phones) to start doing your predictions! Most Machine Learning projects in production will 'train' the model on cloud servers, then 'deploy' the model to an API server or mobile device. This session will introduce the attendee on using TensorFlow Serving and Apple CoreML to deploy Machine Learning models to a mobile app.
Have you ever wondered about how your team operates and their dynamics? Confused on how much (or alittle) wiggle room you should give your team? Should a team have a singular focus or have a broader reach? Wondering if there be a lot of team processes or alittle? This talk will discuss 4 key components to keep in mind when running teams. Attendees, whether they are team leads or team members, will leave with a solid understanding of what makes a team tick and how to improve cohesion. Attendees will also have tangible, direct steps that they can take with their teams immediately.
Want to learn how to have the exact same environment locally as is in production? Docker is an open source tool to organize Linux containers. A container is a sandbox environment that runs a collection of processes. Containers are light-weight VMs that share the same kernel as the host OS. This session will teach attendees the concept of containers, the pros and cons, plus how to get up and running on your machine.
Crank out more bits for the web with automation tools. Learn how to speed up your development process, automate different processes most developers do already, and leverage your build cycle to save time. We'll use a combination of tools to accomplish this with tools like npm, gulp, broswersync, yeoman, and more!
Is your whole team spending a day each getting local environments ready to start work? Are you a web developer with hundreds of active clients and build each VPS by hand? It's time you learned how to leverage DevOps to automate as much work as possible! Attendees will learn about the concept of DevOps, popular tools, and how to get started. We'll talk about tools like Chef, Puppet, and Vagrant.