It’s common for even the most amazing programmers to not have the first clue about math. That makes learning neural networks particularly inaccessible, as an integral part of explaining it relies on mathematical formulas. Ah, the formulas…with all their lines and curves and ancient symbols; they’re just as unintelligible as they are beautiful.
What’s a better way for us to learn it instead? With a language we all speak: code.
Ellen Körbes dives into every component required to write a neural network from scratch, like network structure, activation functions, forward propagation, gradient descent, and backpropagation. But you look at them as programmers: we define what we’re trying to achieve, then write an implementation for it. You do that with no specialized libraries like TensorFlow and PyTorch—only Go code. So if you ever wanted to really understand how a neural network works, but thought it to be out of your reach because of the math, this is for you. Code, not math. Algorithms, not logarithms.
Ellen Körbes works with developer relations at Garden. They code, write, speak, teach Go, make videos, and dabble with Kubernetes. A native of Brazil, they’re deeply involved with diversity and inclusiveness in tech.
Help us make this conference the best it can be for you. Have questions you'd like this speaker to address? Suggestions for issues that deserve extra attention? Feedback that you'd like to share with the speaker and other attendees?
Join the conversation here (requires login)
©2019, O'Reilly Media, Inc. • (800) 889-8969 or (707) 827-7019 • Monday-Friday 7:30am-5pm PT • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. • firstname.lastname@example.org