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

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