1. Explore Flux: FizzBuzz
2. Price Prediction, Math (March 22)
3. Tailoring Flux (April 19)
4. Image Recognition (May 10)
Why an itinerary?
The itinerary is a virtual guide. This optional list describes the highlights of each release to maximize your learning time.
The FluxML machine learning library is an umbrella for several different Julia ML libraries. These cutting edge Auto Differentiation libraries mean your models will learn faster with less training, You can use FluxML to solve computer vision problems, classify data, or predict prices based on working examples from the model zoo, and tailor solutions to your own use. FluxML gives you the option of piecing together predefined loss functions, optimizers, activation functions, and model layers or crafting pieces yourself since it is Julia all the way down.
Put them together and you get an environment that’s purpose built for high performance without sacrificing elegance.
The rise of complexity of ML solutions has led to a greater adoption of general purpose languages, but such solutions had to sacrifice either performance or productivity. Julia was created to step into this gap. FluxML makes use of much of the power of Julia. Language features enable Zygote to rapidly compute the gradients that are the heart of ML systems. Processor compilation support for GPUs and TPUs gives FluxML high performance out of the box without any additional support. Julia’s elegant multiple dispatch allows models, otpimizers, and data processing to resemble the math they are based on.
Groxio courses focus on concepts rather than features. Since Groxio’s audience has more programmers than data scientists, we’ll spend most of our time focusing on breaking through the dense math concepts that are woven through the FluxML documentation to help you build a strong intuition for the concepts behind the curtain.
We’ll start with a FizzBuzz project to demonstrate the concepts of neural networks and FluxML.
When you’re done, you’ll be able to:
Shape your data for FluxML.
Construct layered FluxML models, matching dimensions and including activation functions.
Train a FluxML model to report progress.
Identify the type of ML problems you’re solving, and apply the appropriate FluxML example to problems ranging from home pricing, image detection, classification, and text processing.