yimeng-sun,github

adaptfilt

Adaptive filtering module for Python

adaptive-algorithm-for-noise-cancellation

The design is proposed to get a signal with reduced noise at low frequency. The parameters used here are to simplify the computation and enhance the filtering capability using feed forward Adaptive filter. The filter follows causality so as to control maximum noise reduction and attenuation bandwidth. Systematic analysis is computed to predict noise performance. This methodology uses two 24 tap filter, an FIR filter, and white noise to produce efficient output. Desirable length of the filter is chosen to reduce Hardware complexity. Filter follow a sequential structure to save resources and clock cycles. Two adaptive filters are cascaded to maintain causality. An FxLMS algorithm is proposed in which filter coefficients vary as they update weight functions to the signal to make the system stable.

ads-matlab-interface

Keysight Advanced Design System (ADS) to Matlab interface

alphatree-graphic-deep-neural-network

AI Roadmap:机器学习(Machine Learning)、深度学习(Deep Learning)、对抗神经网络(GAN），图神经网络（GNN），NLP，大数据相关的发展路书(roadmap), 并附海量源码（python，pytorch）带大家消化基本知识点，突破面试，完成从新手到合格工程师的跨越，其中深度学习相关论文附有tensorflow caffe官方源码，应用部分含推荐算法和知识图谱

awesome_deep_learning_interpretability

深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)

build-your-own-x

🤓 Build your own (insert technology here)

classify-radio-signals

Built and trained a convolutional neural network (CNN) in Keras from scratch to classify 2D spectograms of deep space radio signals.

cnn-explainer

Learning Convolutional Neural Networks with Interactive Visualization.

cnn-text-classification-tf

Convolutional Neural Network for Text Classification in Tensorflow

cnnmanipmatconvnet

Code for CNN visualization, RF estimation and more in matlab using MatConvNet toolbox.

convolution-neural-network-for-optimizing-patient-waiting-time

In this project, I created a deep learning models such as Convolution Neural Network (CNN) and Artificial Neural Network (ANN) to predict the optimized time required by a doctors to attend the patient at ER at the hospital. The data set (folder name: DS) used in this project contains 1000 .csv files, each illustrating the time required for 50 doctors and 50 patients. In this project, the proposed model is developed in four steps. Firstly, i performed Pre-processing techniques such as feature extraction and data splitting for training and testing. Secondly, I designed the model using keras libraries. Third, I trained the model by tuning hyper parameters and lastly, i tested model using testing dataset.

deep-learning-hw2

Backpropagation, Optimizations and CNNs

deep-learning-toolbox-cnn

An improved version for matlab deep learning toolbox cnn

deeplearntoolbox

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

dncnn

Implementation of DnCNN in MATLAB using Neural Network Toolbox™

dnn_detection_via_keras

This is the simplest implementation of Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems using keras.

fdnn

Non-linear digital self-interference cancellation for in-band full-duplex radios using neural networks

fe-interview

前端面试每日 3+1，以面试题来驱动学习，提倡每日学习与思考，每天进步一点！每天早上5点纯手工发布面试题（死磕自己，愉悦大家），4000+道前端面试题全面覆盖，HTML/CSS/JavaScript/Vue/React/Nodejs/TypeScript/ECMAScritpt/Webpack/Jquery/小程序/软技能……