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color_detection_basys3

Color Detection using Basys3 FPGA

common-pl-devices-on-pynq

Integration on PL side of Zynq7000 for PYNQ framework of common industrial devices (GPIO, I2C, SPI and UART)

convfpga

convolution_network_on_fpga

CNN acceleration on virtex-7 FPGA with verilog HDL

cosmogan

cs273a-introduction-to-machine-learning

Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

cs_gra-hitsz

哈尔滨工业大学（深圳）计算机科学与技术研究生课程

d5m_camera_zynq

D5M CAMERA using ZYNQ

dac2018-tgiif

The 1st place winner's source codes for DAC 2018 System Design Contest, FPGA Track

darknet

YOLOv4 - Neural Networks for Object Detection (Windows and Linux version of Darknet )

dashee87.github.io

:triangular_ruler: A flexible two-column Jekyll theme. Perfect for personal sites, blogs, and portfolios hosted on GitHub or your own server.

data-science-predict-weather-events

Explore weather event data and use machine learning to predict the damage costs of storm events based on location, time of year, and type of event

deep-learning-for-time-series-data

The examples showcase two ways of using deep learning for classifying time-series data, i.e. ECG data. The first way is using continuous wavelet transform and transfer learning, whereas the second way is using Wavelet Scattering and LSTMs. The explanations of the code are in Chinese. The used data set can be download on：https://github.com/mathworks/physionet_ECG_data/

deep-learning-hardware-accelerator

Paper Collection of Deep Learning Hardware Accelerator

deep-neural-network-hardware-accelerator

SystemVerilog HDL and TB code Deep Neural Network Hardware Accelerator implementation on zybo 7010 FPGA and also C code for Vivado SDK Software

deep-neural-network-hardware-accelerator-1

My implementation of an FPGA Deep Neural Network Hardware Accelerator, moved from my bitbucket

deeplearningbook-chinese

Deep Learning Book Chinese Translation

deepvision

在我很多项目中用到的CV算法推理框架应用。

design-and-implementation-of-face-recognition-based-on-pynq

Face recognition, computer vision, deep learning, PYNQ, Movidius NCS

deye

Keep an Eye on Defects Inspection.

digital-design-with-verilog

Projects done for Advanced Digital Design with Verilog. Examples include code for applications like Sobel Edge Detection and DTMF generation.

digital-logic-design

透過數位邏輯結合VHDL與Verilog的過程，作為從基礎數位邏輯到計算機系統結構，並實作出一顆CPU的教學書籍，希望未來可以成為教學範例檔案。

draw-neural-network-js

draw multi-layer neural network using cavans.

eyerissf

An Eyeriss Chip (researched by MIT, a CNN accelerator) simulator and New DNN framework "Hive"

face-detection-in-pynq

This project will use ZYNQ 7020 to build the PYNQ framework, and build face detection algorithm, and finally use USB camera to detect face.

faster-rcnn-pytorch

这是一个faster-rcnn的pytorch实现的库，可以利用voc数据集格式的数据进行训练。

first-order-model

This repository contains the source code for the paper First Order Motion Model for Image Animation

fpga

帮助大家进行FPGA的入门，分享FPGA相关的优秀文章，优秀项目

fpga-1

数字IC相关资料

fpga-cnn

FPGA implementation of Cellular Neural Network (CNN)