Learning Learning rate | Let's learn – Neural networks from scratch in Go – 29
Today I continue to study SGD optimizer and taking a look into learning rate: how it affects model training and how to find a way to adjust learning rate during model training.
We study the book http://nnfs.io and implementing all the code with Golang instead of Python. The aim is to learn how neural networks are made inside and how it looks like to build is fully from the scratch without using frameworks like pytorch or tensorflow. We’ll have to find alternative in Go or reimplement some operations fully from the ground.
🖥️ You can support my channel, subscribe for more content or write directly to me: https://ko-fi.com/roman_v
📹 Main channel: https://www.youtube.com/@_RomanV_
✉️ Contact: roman.volkov.code@gmail.com
💻 Code: https://github.com/RomanVolkov/nnfs_go
👀 Disclaimer:
Views and opinions are my own and do not represent or reflect the opinions of my current or past employer(s).
#learncoding #softwareengineering #neuralnetwork #machinelearning #ai #golang
Today I continue to study SGD optimizer and taking a look into learning rate: how it affects model training and how to find a way to adjust learning rate during model training.
We study the book http://nnfs.io and implementing all the code with Golang instead of Python. The aim is to learn how neural networks are made inside and how it looks like to build is fully from the scratch without using frameworks like pytorch or tensorflow. We’ll have to find alternative in Go or reimplement some operations fully from the ground.
🖥️ You can support my channel, subscribe for more content or write directly to me: https://ko-fi.com/roman_v
📹 Main channel: https://www.youtube.com/@_RomanV_
✉️ Contact: roman.volkov.code@gmail.com
💻 Code: https://github.com/RomanVolkov/nnfs_go
👀 Disclaimer:
Views and opinions are my own and do not represent or reflect the opinions of my current or past employer(s).
#learncoding #softwareengineering #neuralnetwork #machinelearning #ai #golang