Deep Learning practice - Logistic Regression

• We use an easier way to reduce the complexity of the programs for beginners who study machine learning. Therefore, we hope it can be easier to understand and implement the multi-layer computation of backpropagation.
• These programs are basically modified from the exams in "Neural Networks and Deep Learning" on Coursera. If you want a comprehensive explanation or any tutorial videos, I recommended you enroll in the course "Neural Networks and Deep Learning" on Coursera.
• If you need more information or you are a beginner in deep learning, you could go to my website.
  • BrilliantCode.net
  • Backpropagation(BP) 倒傳遞法 #1 工作原理與說明
  • Backpropagation(BP) 倒傳遞法 #2 貓貓分類器-2層類神經網路
  • Backpropagation(BP) 倒傳遞法 #3 貓貓分類器-N層類神經網路

References

• "Neural Networks and Deep Learning" on Coursera.

Programs

2-Layer Logistic Regression: Cat classifier

• File: logistic_regression_2-layer.ipynb

n-Layer Logistic Regression: Cat classifier

• File: logistic_regression_N-layer.ipynb

How to use?

• Just fork to your own github or download this repository directly. And run the jupyter file I just mentioned in the "Programs" section.