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

gradient descent in machine learning python | 1.77 | 0.2 | 449 | 36 |

gradient descent in machine learning | 0.63 | 0.4 | 3836 | 59 |

what is gradient descent in machine learning | 1.36 | 0.1 | 9524 | 82 |

types of gradient descent in machine learning | 0.54 | 1 | 8582 | 81 |

gradient descent in machine learning code | 0.14 | 0.1 | 2285 | 26 |

gradient descent machine learning example | 0.01 | 0.8 | 8403 | 17 |

how to implement gradient descent in python | 1.85 | 0.2 | 4153 | 11 |

implementing gradient descent in python | 0.42 | 0.8 | 1587 | 65 |

gradient descent implementation in python | 1.74 | 0.5 | 7493 | 40 |

gradient descent in python | 1.42 | 0.8 | 7622 | 79 |

gradient descent algorithm in python | 1.61 | 0.8 | 842 | 71 |

gradient descent using python | 0.88 | 0.3 | 8556 | 60 |

gradient descent method python | 0.11 | 1 | 4327 | 25 |

gradient descent python example | 0.39 | 0.2 | 4001 | 83 |

gradient descent function python | 1.98 | 0.8 | 9537 | 68 |

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.

Gradient is a commonly used term in optimization and machine learning. For example, deep learning neural networks are fit using stochastic gradient descent, and many standard optimization algorithms used to fit machine learning algorithms use gradient information. In order to understand what a gradient is, you need to understand what a derivative is from the field of calculus.