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awesome-bayesian-deep-learning

A curated list of resources dedicated to bayesian deep learning

bayesianlearning

Bayesian Machine Learning

bpl

Bayesian Program Learning model for one-shot learning

caffe

Caffe for Structurally Sparse Deep Neural Networks

caffe_code_analysis

Caffe_Code_Analysis

cnn-accelerator-vlsi

Convolutional accelerator kernel, target ASIC & FPGA

cnn-fpga

Implementation of CNN on ZYNQ FPGA to classify handwritten numbers using MNIST database

cnn_hardware_acclerator_for_fpga

This is a fully parameterized verilog implementation of computation kernels for accleration of the Inference of Convolutional Neural Networks on FPGAs

cs231n.github.io

Public facing notes page

dnn_neurosim_v2.0

Benchmark framework of compute-in-memory based accelerators for deep neural network

edward

A library for probabilistic modeling, inference, and criticism. Deep generative models, variational inference. Runs on TensorFlow.

fpgaandcnn

基于FPGA的数字识别-实时视频处理的定点卷积神经网络实现

hardware_mlp_verilog_cad

MLP Hardware Implementation - Computer Aided Design Course - University of Tehran - Dr. Modarresi

hdl

HDL libraries and projects

leetcode

The answer of LeetCode

ml-visuals

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

mlp-verilog

Multi-layered perceptron neural network implemented in verilog.

mnist_cnn_hdl

nthu-iclab

清華大學 | 積體電路設計實驗 (IC LAB) | 110上

probabilistic-programming-and-bayesian-methods-for-hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

pydatalondon2017

Bayesian Deep Learning with Edward (and a trick using Dropout)

pymc

PyMC: Bayesian Stochastic Modelling in Python (for PyMC3: https://github.com/pymc-devs/pymc3)

python

scsynth

Synthesis tool for stochastic computing

st3_with_cpp

This Repo describes the method of compiling and running cpp file in sublime text 3