Christopher Bonnett's repositories

deep_learning_cookbook

Deep Learning Cookbox

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02456-deep-learning

Exercises and supplementary material for the deep learning course 02456.

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bayesian_dense

Bayesian Weight Uncertainty Dense Layer for Keras

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bottleneck

Fast NumPy array functions written in C

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CompStats

Code for a workshop on statistical interference using computational methods in Python.

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dist-keras

Distributed deep learning with Keras and Apache Spark.

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efficientnet

Implementation on EfficientNet model. Keras.

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genomeview

An extensible python-based genomics visualization engine

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gpbo

gpbo

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kaggle-facial-keypoints-detection

Kaggle "Facial Keypoints Detection" competition.

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keras-tuner

Hyperparameter tuning for humans

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llama_index

LlamaIndex (GPT Index) is a data framework for your LLM applications

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LSSTC-DSFP-Sessions

Lecture slides, Jupyter notebooks, and other material from the LSSTC Data Science Fellowship Program

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minimal_vae

A minimal implementation of an Variational Auto-Encoder

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missingno

Missing data visualization module for Python.

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Mixture-Density-Networks-for-distribution-and-uncertainty-estimation

A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)

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mxnet-the-straight-dope

An interactive book on deep learning. Much easy, so MXNet. Wow.

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NPEET

Non-parametric Entropy Estimation Toolbox

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numpy-100

100 numpy exercises (100% complete)

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pandas-workshop

An introductory workshop on pandas with notebooks and exercises for following along.

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plan-net

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

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resources

PyMC3 educational resources

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rst-cheatsheet

A two-page cheatsheet for restructured text

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streamlit-example

Example Streamlit app that you can fork to test out share.streamlit.io

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UMAL

Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians

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vadam

Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, Gal, and Srivastava

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