This video describes the details of the gradient descent algorithm and includes code for modeling using it.
What You’ll Learn:
1. The fundamentals of gradient descent algorithm
2. How to prepare and preprocess your dataset for analysis
3. Step-by-step coding using popular libraries like NumPy, Pandas, and Scikit-learn
4. Tips for visualizing your data and results
5. How to evaluate the performance of your model.
Previous videos:
1. Linear Regression Model in Python: A Step-by-Step Guide
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2. How to calculate RMSE, Correlation, and other metrics in Python | Code
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3. How to develop a Multiple Regression Model in Matlab
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4. Flow direction concept and code
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5. Wavelet Neural Network
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6. Concept of Extreme learning machines (ELM)
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(00:00) Introduction
(00:49) Description of Gradient descent algorithm
(06:01) Python inputs
(07.30) Model development
(13:17) Model loops
(12:48) Model development
(19:00) Model run
(20:38) Model testing
(24:37) Importance of Epochs
(25:54) Conclusions