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

gradient descent implementation in python | 0.31 | 1 | 9181 | 62 | 41 |

gradient | 0.24 | 0.6 | 579 | 61 | 8 |

descent | 1.4 | 1 | 5311 | 49 | 7 |

implementation | 1.73 | 1 | 1473 | 25 | 14 |

in | 0.44 | 0.7 | 9137 | 22 | 2 |

python | 1.55 | 0.3 | 9269 | 56 | 6 |

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

gradient descent implementation in python | 0.93 | 1 | 4089 | 26 |

gradient descent code in python | 0.63 | 0.4 | 3284 | 40 |

gradient descent algorithm in python | 0.17 | 0.6 | 356 | 56 |

gradient descent python library | 1.36 | 0.5 | 5009 | 18 |

gradient descent algorithm python code | 1.25 | 0.6 | 4401 | 39 |

stochastic gradient descent python code | 1.21 | 0.4 | 6055 | 86 |

mini batch gradient descent python code | 0.88 | 0.6 | 8159 | 10 |

gradient descent python code github | 0.56 | 0.3 | 8377 | 81 |

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.