index.md

layout	mathjax	title
default	true	Probabalistic Deep Learning with Python

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 in https://github.com/oduerr/dl_book_docker/blob/master/README.md

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
2a	MNIST digit classification with fcNN	nb_ch02_02a	nb_ch02_02a
3	CNN edge lover	nb_ch02_03	nb_ch02_03
4	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_tf2	nb_ch03_03 nb_ch03_03_tf2
4	Backpropagation by hand	nb_ch03_04 nb_ch03_04_tf2	nb_ch03_04 nb_ch03_04_tf2
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 die	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

Chapter 5: Probabilistic deep learning models with TensorFlow Probability

Number	Topic	Github	Colab
1	Modelling continuous data with Tensoflow Probability	nb_ch05_01	nb_ch05_01
2	Modelling count data with Tensoflow Probability	nb_ch05_02	nb_ch05_02

Chapter 6: Probabilistic deep learning models in the wild

Number	Topic	Github	Colab
1	Discretized Logistic Mixture distribution	nb_ch06_01	nb_ch06_01
2	Regressions on the deer data	nb_ch06_02	nb_ch06_02
3	Getting started with flows	nb_ch06_03	nb_ch06_03
4	Using RealNVP	nb_ch06_04	nb_ch06_04
5	Fun with glow	nb_ch06_05	nb_ch06_05

Chapter 7: Bayesian learning

Number	Topic	Github	Colab
1	Predict images with a pretrained Imagenet network	nb_ch07_01	nb_ch07_01
2	Bayes Linear Regression Brute Force vs Analytical	nb_ch07_02	nb_ch07_02
3	Bayesian model for a coin toss	nb_ch07_03	nb_ch07_03
4	Play with the analytical Bayes solution for linear regression	nb_ch07_04	nb_ch07_04

Chapter 8: Bayesian neural networks

Number	Topic	Github	Colab
1	Linear Regression the Bayesian way	nb_ch08_01	nb_ch08_01
2	Dropout to fight overfitting	nb_ch08_02	nb_ch08_02
3	Regression case study with Bayesian Neural Networks	nb_ch08_03	nb_ch08_03
4	Classification case study with novel class	nb_ch08_04	nb_ch08_04