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aalto-dl-for-physical-layer

An Introduction to Deep Learning for the Physical Layer vs End-to-End Learning of Communications Systems Without a Channel Model

adversarial

Code and hyperparameters for the paper "Generative Adversarial Networks"

autoencoder-based-communication-system

Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/

autoencoder_for_physical_layer

This is my attempt to reproduce and extend the results in the paper "An Introduction to Deep Learning for the Physical Layer" by Tim O'Shea and Jakob Hoydis

autokeras

AutoML library for deep learning

awesome-machine-learning

A curated list of awesome machine Learning tutorials,courses and communities.

awesome-papers

机器学习，深度学习，自然语言处理，计算机视觉方面的顶级期刊会议论文集

channel-estimation

记录毕设实验代码

channelnet

Implementation of the paper "Deep Learning-Based Channel Estimation"

communication-simulation

communication simulation (Communication Signal Processing LAB)

deeplearning

Python implementation of Deep Learning book

dl_ofdm

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks

dlforphy

end-to-end-learning-of-communications-systems-without-a-channel-model

Implementation of research paper: End-to-End Learning of Communications Systems Without a Channel Model

end2end_gan

Conditional GAN based End-to-End Communication System

fitting-gaussian-mixture-models-with-em

Implement the EM algorithm for a Gaussian mixture model and apply it to cluster images

gaussian-mixture-model

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

jcm-awgn-imp

Results submitted to ICASSP 2020 for the paper titled "Joint Coding and Modulation in the Ultra-Short Blocklength Regime for Bernoulli-Gaussian Impulsive Noise Channels Using Autoencoders"

keras

Deep Learning for humans

keras-gan

Keras implementations of Generative Adversarial Networks.

linux-command

Linux命令大全搜索工具，内容包含Linux命令手册、详解、学习、搜集。https://git.io/linux

machine-learning-algorithms-scratch

This repo contains the codes of various standard machine learning algorithms implemented from scratch in python

master-thesis

Source Code to my master's thesis with the topic "End-to-end optimisation of MIMO systems using deep learning autoencoders"

mgan

Source code for the paper MGAN: Training Generative Adversarial Nets With Multiple Generators

ml-from-scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

ml-in-physical-layer

ml_wirelesscomm

Machine Learning Applications in Wireless Communications - Project work

models

Models and examples built with TensorFlow

multipath_simulation

:signal_strength: Using Python to simulate multipath fading channel