Keyword | CPC | PCC | Volume | Score | Length of keyword |
---|---|---|---|---|---|

projected gradient descent method | 1.93 | 0.1 | 4023 | 22 | 33 |

projected | 1.58 | 0.3 | 1988 | 46 | 9 |

gradient | 0.93 | 0.1 | 8899 | 20 | 8 |

descent | 0.22 | 0.3 | 796 | 18 | 7 |

method | 0.09 | 0.5 | 7827 | 88 | 6 |

Linear regression does provide a useful exercise for learning stochastic gradient descent which is an important algorithm used for minimizing cost functions by machine learning algorithms. As stated above, our linear regression model is defined as follows: y = B0 + B1 * x.

Gradient descent is simply used in machine learning to find the values of a function's parameters (coefficients) that minimize a cost function as far as possible. You start by defining the initial parameter's values and from there gradient descent uses calculus to iteratively adjust the values so they minimize the given cost-function.