Machine Learning & Deep Learning Fundamentals

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Backpropagation explained | Part 5 - What puts the "back" in backprop?

March 14, 2018 by


Let's see the math that explains how backpropagation works backwards through a neural network. We’ve seen how to calculate the gradient of the loss function using backpropagation in the previous video. We haven’t yet seen though where the backwards movement comes into play that we talked about when we discussed the intuition for backprop. So now, we’re going to build on the knowledge that we’ve already developed to understand what exactly puts the back in backpropagation. The explanation we’ll give for this will be math-based, so we’re first going to start out by exploring the motivation needed for us to understand the calculations we’ll be working through. We’ll then jump right into the calculations, which, we’ll see, are actually quite similar to ones we’ve worked through in the previous video. After we’ve got the math down, we’ll then bring everything together to achieve the mind-blowing realization for how these calculations are mathematically done in a backwards fashion. 💥🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎💥 👉 Check out the blog post and other resources for this video: 🔗 💻 DOWNLOAD ACCESS TO CODE FILES 🤖 Available for members of the deeplizard hivemind: 🔗 🧠 Support collective intelligence, join the deeplizard hivemind: 🔗 🤜 Support collective intelligence, create a quiz question for this video: 🔗 🚀 Boost collective intelligence by sharing this video on social media! ❤️🦎 Special thanks to the following polymaths of the deeplizard hivemind: Peder B. Helland 👀 Follow deeplizard: Twitter: Facebook: Patreon: YouTube: Instagram: 🎓 Other deeplizard courses: Reinforcement Learning - NN Programming - DL Fundamentals - Keras - TensorFlow.js - Data Science - Trading - 🛒 Check out products deeplizard recommends on Amazon: 🔗 📕 Get a FREE 30-day Audible trial and 2 FREE audio books using deeplizard’s link: 🔗 🎵 deeplizard uses music by Kevin MacLeod 🔗 🔗 ❤️ Please use the knowledge gained from deeplizard content for good, not evil.