dlzln

dlzln

Config files for my GitHub profile.

Coursera-ML-AndrewNg-Notes

吴恩达老师的机器学习课程个人笔记

deap-cnn-lstm

Emotion recognition based on DEAP dataset using One-Dimensional CNN, dan RNN (GRU, and LSTM).

EEG-Based-Emotion-Recognition-on-DEAP

Automatic-Emotion-Recognition-on-DEAP-Dataset

The project uses EEG signals from the DEAP Dataset to classify emotions into 4 classes using Ensembled 1-D CNNs, LSTMs and 2D , 3D CNNs and Cascaded CNNs with LSTMs.

Emotion-Recognition-from-brain-EEG-signals-

Emotion recognition can be achieved by obtaining signals from the brain by EEG . This test records the activity of the brain in form of waves. We have used DEAP dataset on which we are classifying the emotion as valance, likeness/dislike, arousal, dominance. We have used LSTM and CNN classifier which gives 88.60 % accuracy to predict the model successfully.

omnetpp_primer

OMNeT++的仿真手册

Awesome-Chinese-NLP

A curated list of resources for Chinese NLP 中文自然语言处理相关资料

Brain_typing

Codes, data for brain typing paper.

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

Deep-Learning-with-PyTorch-Tutorials

深度学习与PyTorch入门实战视频教程

pytorch-handbook

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

deeplearning-assignment

吴恩达-深度学习-课后作业-答案与总结

Libsvm-FarutoUltimate-Version

Libsvm-FarutoUltimate Version

Andrew-Ng-Deep-Learning-notes

吴恩达《深度学习》系列课程笔记及代码

AndrewNgMachineLearning

matlab版的代码实现已全部完成吴恩达机器学习

lstm

Minimal, clean example of lstm neural network training in python, for learning purposes.

char-rnn

Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

sentence-completion

A pytorch implementation of the word-level recurrent neural network for sentence completion

AndrewNg-DeepLearning

微专业: 吴恩达 深度学习工程师 作业

WSN_LEECH_Potocol

Implementation of LEACH (Low-energy adaptive clustering hierarchy) Protocols

NegotiationSpin

The Spin mode source for the proposed negotiation protocol.

Multi-target_Location_and_Tracking_with_WSN

Multi-target localization and tracking with Wireless Sensor Networks for digital-storytelling

neuraltalk

NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

WSN-localization

WSN, Localization, APIT, DV-HOP, MDS-MAP

event-related-desynchronization

Motor imagery EEG classifier for my master's thesis.

learning_to_execute

Learning to Execute

WirelessSensorNetwork_Protocols

Implementation of major WSN protocols: SPIN, Gossip, Flooding. Their performance analyses and comparison.