benelot / eth-machine-learning-summary
We cherry-pick the most understandable explanations and definitions into one summary to summarize the content of the lecture about Machine Learning of Prof. Joachim Buhmann.
☆43Updated 8 years ago
Alternatives and similar repositories for eth-machine-learning-summary:
Users that are interested in eth-machine-learning-summary are comparing it to the libraries listed below
- Materials for Bayesian Methods in Machine Learning Course☆88Updated 4 months ago
- NeurIPS workshop on Advances in Approximate Bayesian Inference☆48Updated this week
- ☆72Updated 5 years ago
- Notes for ICML 2019☆38Updated 5 years ago
- ☆59Updated 6 years ago
- A structured list of resources about Sum-Product Networks (SPNs)☆253Updated 4 years ago
- ☆29Updated 5 years ago
- Notebooks explaining the intuition behind the Expectation Maximisation algorithm☆38Updated 5 years ago
- Off the convex path☆67Updated 2 years ago
- https://qdata.github.io/deep2Read/ This website includes a (growing) list of papers and lectures we read on deep learning and relate…☆54Updated 8 months ago
- Differentiable Optimization-Based Modeling for Machine Learning☆333Updated 5 years ago
- Practice with MCMC methods and dynamics (Langevin, Hamiltonian, etc.)☆42Updated 5 years ago
- Practical sessions for the Optimal Transport and Machine learning course at DS3 2018☆89Updated 6 years ago
- Gaussian Processes in Pytorch☆75Updated 5 years ago
- Source for experiments in the Additive Gaussian process paper, as well as extensions relating to dropout.☆21Updated 11 years ago
- Some example scripts on pytorch☆197Updated 3 years ago
- Projects of Advanced Machine Learning, ETH Zürich, Fall 2018☆9Updated 6 years ago
- ☆122Updated 2 years ago
- Course Repository for CS 375 (Large Scale Neural Network Modules for Neuroscience)☆42Updated last week
- ☆24Updated 4 years ago
- hessian in pytorch☆187Updated 4 years ago
- Small Python library to automatically set CUDA_VISIBLE_DEVICES to the least loaded device on multi-GPU systems.☆106Updated 2 years ago
- Code for NeurIPS 2019 paper: "Symmetry-Based Disentangled Representation Learning requires Interaction with Environments" by H. Caselles-…☆35Updated 5 years ago
- Understanding normalizing flows☆131Updated 5 years ago
- ☆124Updated 6 years ago
- ☆36Updated 9 years ago
- Experiments for the paper "Exponential expressivity in deep neural networks through transient chaos"☆70Updated 8 years ago
- Group elastic net implementation in PyTorch.☆45Updated 4 years ago
- Implementation and evaluation of different approaches to get uncertainty in neural networks☆140Updated 7 years ago
- NYU PSYCH-GA 3405.001 / DS-GA 3001.014 : Advancing AI through cognitive science☆131Updated 5 years ago