Each of the four subfields are contextualized with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. Chapters build upon one another, with foundational topics such as Bayes' theorem followed by more advanced concepts, like training neural networks using vectors, matrices, and derivatives of functions. You'll ultimately put all this math to use as you explore and implement deep learning algorithms, including backpropagation and gradient descent - the foundational algorithms that have enabled the AI revolution.

You'll learn: The rules of probability, probability distributions, and Bayesian probability; The use of statistics for understanding datasets and evaluating models; How to manipulate vectors and matrices, and use both to move data through a neural network; How to use linear algebra to implement principal component analysis and singular value decomposition; How to apply improved versions of gradient descent, like RMSprop, Adagrad and Adadelta.

Once you understand the core math concepts presented throughout this book through the lens of AI programming, you'll have foundational know-how to easily follow and work with deep learning.

## Book Details | |

Publisher: | No Starch Press |

By: | Ronald T. Kneusel |

ISBN-13: | 9781718501904 |

ISBN-10: | 1718501900 |

Year: | 2021 |

Pages: | 344 |

Language: | English |

## Book Preview | |

Online | Math for Deep Learning |

## Free Download | |

Chapter 11 | Math for Deep Learning |

Source Code | Math for Deep Learning |

## Paper Book | |

Buy: | Math for Deep Learning |

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