sinzlab/data-science-tutorials-ws21

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sinzlab/data-science-tutorials-ws21

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Contents for the tutorials of the data science lecture, winter semester 2021/22

sensorium

Code base for the SENSORIUM competition.

Jupyter Notebook

jinja-compose

Docker compose extension with Jinja based templating support.

neuralpredictors

Machine Learning Utils of Sinzlab

Jupyter Notebook

nnfabrik

A generalized model fitting pipeline that houses models, trainers, and datasets in datajoint and returns as well as stores trained models.

Jupyter Notebook

pytorch-docker

Docker image for PyTorch based on nvidia/cuda images

neural_cotraining

The code for performing MTL on object recognition with neural data

cGNF

This is the official code for calibration in multi-hypothesis human pose estimation

Jupyter Notebook

Lurz_2020_code

Code base for "Generalization in data-driven models of primary visual cortex", Lurz et al. 2020

Jupyter Notebook

platypose

Official Implementation for "Platypose: Calibrated Zero-Shot Multi-Hypothesis 3D Human Motion Estimation"

Jupyter Notebook

orbit_transfer

nndichromacy

Understanding color processing in vouse V1 using Deep Neural Networks. In collaboration with Katrin Franke and the BCM.

Jupyter Notebook

mei

Visualize features cells are responsive to via gradient ascent.

bashiri-et-al-2021

Code for models used in Bashiri et al., "A Flow-based latent state generative model of neural population responses to natural images".

insilico-stimuli

A toolset for generating image stimuli for insilico experiments

Jupyter Notebook

propose

PRObabilistic POSE estimation framework

Jupyter Notebook

datajoint-cleaner

Sinz2018_NIPS

Code for the NIPS 2018 paper: "Stimulus domain transfer in recurrent models for large scale cortical population prediction on video"

nnvision

Envisioning the biological visual system with deep neural networks.

essential-mathematics-script

Lecture notes for essential mathematics for neuroscience lecture held at the Graduate School for Neural and Behavioural Sciences in Tübingen with Jakob Macke and Philipp Lies.

energy-guided-diffusion

🥚 EnerGy Guided Diffusion for optimizing neurally exciting images

Jupyter Notebook

causal_neuroscience

Collection of introductory study material for people who are interested to answer causal questions in the field of neuroscience.

Jupyter Notebook

nntransfer

Framework for transfer experiments. Builds on top of nnfabrik and neuralpredictors.

pytorch-singularity

phd-thesis-template-uni-tuebingen

PhD thesis template for Uni Tübingen

hlrn-slurm

lurz_bashiri_iclr2023

Jupyter Notebook

HARD

Hard Augmentations for Robust Distillation

datajoint-link

tljh-db

nnsysident

System identification with Deep Neural Networks

Jupyter Notebook

datajoint-webinar

Jupyter Notebook