# dl book notebooks overview

You can use the notebooks below by clicking on the Colab Notebooks link or running them locally on your machine.

To run them locally, you can either

- install the required software (Python with TensorFlow) or
- use the provided Docker container as described here [Link will follow upon finishing the book].

## Chapter 2: Neural network architectures

Number | Topic | Github | Colab |
---|---|---|---|

1 | Banknote classification with fcNN | nb_ch02_01 | nb_ch02_01 |

2 | MNIST digit classification with shuffling | nb_ch02_02 | nb_ch02_02 |

3 | MNIST digit classification with fcNN | nb_ch02_02a | nb_ch02_02a |

4 | CNN edge lover | nb_ch02_03 | nb_ch02_03 |

5 | Causal and time dilated convolutions | nb_ch02_04 | nb_ch02_04 |

## Chapter 3: Principles of curve fitting

Number | Topic | Github | Colab |
---|---|---|---|

1 | Gradient descent method for linear regression with one tunable parameter | nb_ch03_01 | nb_ch03_01 |

2 | Gradient descent method for linear regression | nb_ch03_02 | nb_ch03_02 |

3 | Linear regression with TensorFlow | nb_ch03_03 | nb_ch03_03 |

4 | Backpropagation by hand | nb_ch03_04 | nb_ch03_04 |

5 | Linear regression with Keras | nb_ch03_05 | nb_ch03_05 |

6 | Linear regression with TF Eager | nb_ch03_06 | nb_ch03_06 |

7 | Linear regression with Autograd | nb_ch03_07 | nb_ch03_07 |

## Chapter 4: Building loss functions with the likelihood approach

Number | Topic | Github | Colab |
---|---|---|---|

1 | First example of the maximum likelihood principle: throwing a dice | nb_ch04_01 | nb_ch04_01 |

2 | Calculation of the loss function for classification | nb_ch04_02 | nb_ch04_02 |

3 | Calculation of the loss function for regression | nb_ch04_03 | nb_ch04_03 |

4 | Regression fit for non-linear relationships with non-constant variance | nb_ch04_04 | nb_ch04_04 |