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

gradient descent in simple linear regression | 1.45 | 0.5 | 2688 | 68 | 44 |

gradient | 0.52 | 0.4 | 3001 | 99 | 8 |

descent | 0.4 | 0.7 | 2735 | 52 | 7 |

in | 1.45 | 0.7 | 9792 | 91 | 2 |

simple | 0.4 | 0.2 | 3766 | 48 | 6 |

linear | 0.53 | 0.7 | 9063 | 36 | 6 |

regression | 1.82 | 0.9 | 896 | 18 | 10 |

Keyword | CPC | PCC | Volume | Score |
---|---|---|---|---|

gradient descent in simple linear regression | 1.85 | 0.9 | 8255 | 48 |

gradient descent algorithm linear regression | 1.35 | 0.2 | 9136 | 42 |

linear regression gradient descent derivation | 1.66 | 0.6 | 527 | 67 |

linear regression and gradient descent | 1.87 | 0.2 | 578 | 7 |

gradient descent linear regression example | 1.82 | 0.9 | 4211 | 79 |

define gradient descent for linear regression | 1.02 | 0.6 | 6207 | 59 |

gradient descent vs linear regression | 0.92 | 0.3 | 6864 | 7 |

what is gradient descent in linear regression | 0.74 | 0.5 | 7902 | 5 |

gradient descent linear regression calculator | 1.48 | 1 | 4181 | 96 |

gradient descent linear regression formula | 0.63 | 1 | 1238 | 46 |

multiple linear regression gradient descent | 0.51 | 0.2 | 5446 | 7 |

gradient descent algorithm pdf | 0.17 | 0.7 | 7476 | 14 |

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.

The key intuition from gradient descent is that it takes the fastest route towards the minimum point from each step to converge fast. It is done by taking the partial derivative at each step to find the direction towards the local minimum. The graph below explains the intuition for a function with only one parameter.