Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.

Last update: Jan 08, 2023

Overview

Think Bayes 2

by Allen B. Downey

Think Bayes is an introduction to Bayesian statistics using computational methods.

Think Bayes is a Free Book. It is available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), which means that you are free to copy and modify it, as long as you attribute the work and don’t use it for commercial purposes.

Other Free Books by Allen Downey are available from Green Tea Press.

Run the notebooks

Download the notebooks as a Zip file

Or use these links to run the notebooks on Colab:

Owner

Allen Downey

Professor at Olin College, author of Think Python, Think Bayes, Think Stats, and other books. Blog author of Probably Overthinking It.

GitHub Repository http://allendowney.github.io/ThinkBayes2/

D-NeRF: Neural Radiance Fields for Dynamic Scenes

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This project is based on RIFE and aims to make RIFE more practical for users by adding various features and design new models

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190 Jan 08, 2023

A working implementation of the Categorical DQN (Distributional RL).

Categorical DQN. Implementation of the Categorical DQN as described in A distributional Perspective on Reinforcement Learning. Thanks to @tudor-berari

98 Sep 20, 2022

Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust.

Subspace Adversarial Training Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust. However,

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The author's officially unofficial PyTorch BigGAN implementation.

BigGAN-PyTorch The author's officially unofficial PyTorch BigGAN implementation. This repo contains code for 4-8 GPU training of BigGANs from Large Sc

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Hypercomplex Neural Networks with PyTorch

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The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"

Swin-Unet The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"(https://arxiv.org/abs/2105.05537). A validatio

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Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision (ICCV 2021)

Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision (ICCV 2021) PyTorch implementation of Learning RAW-to-sRGB Mappings with Inaccurat

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A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196

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JAXDL: JAX (Flax) Deep Learning Library

JAXDL: JAX (Flax) Deep Learning Library Simple and clean JAX/Flax deep learning algorithm implementations: Soft-Actor-Critic (arXiv:1812.05905) Transf

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Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

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Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.

Related tags

Overview

Think Bayes 2

Run the notebooks

Owner

Allen Downey

D-NeRF: Neural Radiance Fields for Dynamic Scenes

This project is based on RIFE and aims to make RIFE more practical for users by adding various features and design new models

A working implementation of the Categorical DQN (Distributional RL).

Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust.

The author's officially unofficial PyTorch BigGAN implementation.

Hypercomplex Neural Networks with PyTorch

The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"

Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision (ICCV 2021)

A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196

JAXDL: JAX (Flax) Deep Learning Library

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

[NeurIPS 2021] A weak-shot object detection approach by transferring semantic similarity and mask prior.

A lightweight deep network for fast and accurate optical flow estimation.

Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation.

Codes for SIGIR'22 Paper 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation'

Implementation of H-UCRL Algorithm

Keras Image Embeddings using Contrastive Loss

MoveNet Single Pose on OpenVINO

PyTorch code for Composing Partial Differential Equations with Physics-Aware Neural Networks

Collision risk estimation using stochastic motion models