├── README.md ├── notebooks ├── Notebook 1 - Understanding the KLD.ipynb ├── Notebook 2 - Bayesian Linear Regression.ipynb ├── Notebook 3 - Bayesian Logistic Regression, Laplace Approximation.ipynb ├── Notebook 4 - Bayesian Logistic Regression, ELBO.ipynb ├── Notebook 5 - Bayesian Neural Network.ipynb ├── Notebook 6 - Variational Autoencoders.ipynb ├── Notebook 7 - Discriminators.ipynb ├── Notebook 8 - Generative Adversarial Networks.ipynb └── Notebook 9 - Generative Adversarial Networks.ipynb └── slides.pdf /README.md: -------------------------------------------------------------------------------- 1 | # Materials for my talk, Deep Probabilistic Methods with Pytorch 2 | -------------------------------------------------------------------------------- /notebooks/Notebook 1 - Understanding the KLD.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chrisorm/pydata-2018/HEAD/notebooks/Notebook 1 - Understanding the KLD.ipynb -------------------------------------------------------------------------------- /notebooks/Notebook 2 - Bayesian Linear Regression.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chrisorm/pydata-2018/HEAD/notebooks/Notebook 2 - Bayesian Linear Regression.ipynb -------------------------------------------------------------------------------- /notebooks/Notebook 3 - Bayesian Logistic Regression, Laplace Approximation.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chrisorm/pydata-2018/HEAD/notebooks/Notebook 3 - Bayesian Logistic Regression, Laplace Approximation.ipynb -------------------------------------------------------------------------------- /notebooks/Notebook 4 - Bayesian Logistic Regression, ELBO.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chrisorm/pydata-2018/HEAD/notebooks/Notebook 4 - Bayesian Logistic Regression, ELBO.ipynb -------------------------------------------------------------------------------- /notebooks/Notebook 5 - Bayesian Neural Network.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chrisorm/pydata-2018/HEAD/notebooks/Notebook 5 - Bayesian Neural Network.ipynb -------------------------------------------------------------------------------- /notebooks/Notebook 6 - Variational Autoencoders.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chrisorm/pydata-2018/HEAD/notebooks/Notebook 6 - Variational Autoencoders.ipynb -------------------------------------------------------------------------------- /notebooks/Notebook 7 - Discriminators.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chrisorm/pydata-2018/HEAD/notebooks/Notebook 7 - Discriminators.ipynb -------------------------------------------------------------------------------- /notebooks/Notebook 8 - Generative Adversarial Networks.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chrisorm/pydata-2018/HEAD/notebooks/Notebook 8 - Generative Adversarial Networks.ipynb -------------------------------------------------------------------------------- /notebooks/Notebook 9 - Generative Adversarial Networks.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chrisorm/pydata-2018/HEAD/notebooks/Notebook 9 - Generative Adversarial Networks.ipynb -------------------------------------------------------------------------------- /slides.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chrisorm/pydata-2018/HEAD/slides.pdf --------------------------------------------------------------------------------