{"blocks": [{"key": "63ccf928", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "0de1fd5b", "text": "Building a linear regression model from scratch; parameters are optimized through batch gradient descent.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "07646748", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "604cad0a", "text": "Write Python-style pseudocode for batch gradient descent that minimizes mean-squared error for linear regression. Explain briefly what each step in your pseudocode does.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "61462ecf", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "94111f25", "text": "Cover initialization, prediction, error, gradient, parameter update, loop/stop conditions.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}