PoLun-Wang/DL_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.

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.