ZS219274

AlexNet

AlexNet keras implementation

card-crnn-ctpn

CTPN+CRNN bank card number identification(data/test pictures accuary ≈90%)

Classification-of-Environmental-Sound-using-Deep-Learning

Classification of Environment Sound using CNN and ImageDataGenerator

cnn-text-classification-tf

Convolutional Neural Network for Text Classification in Tensorflow

deep-learning-HAR

Convolutional and LSTM networks to classify human activity

dlaicourse

Notebooks for learning deep learning

examples

TensorFlow examples

hellow-world

ImageAI

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

muduo

Event-driven network library for multi-threaded Linux server in C++11

ZS219274/muduo

Event-driven network library for multi-threaded Linux server in C++11

ZS219274/AlexNet

AlexNet keras implementation

ZS219274/card-crnn-ctpn

CTPN+CRNN bank card number identification(data/test pictures accuary ≈90%)

ZS219274/Classification-of-Environmental-Sound-using-Deep-Learning

Classification of Environment Sound using CNN and ImageDataGenerator

ZS219274/cnn-text-classification-tf

Convolutional Neural Network for Text Classification in Tensorflow

ZS219274/deep-learning-HAR

Convolutional and LSTM networks to classify human activity

ZS219274/dlaicourse

Notebooks for learning deep learning

ZS219274/examples

TensorFlow examples

ZS219274/hellow-world

ZS219274/ImageAI

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

ZS219274/keras_ocr

用keras实现OCR定位、识别

ZS219274/MedicalQA-CNN-BiGRU

ZS219274/mnistCRNN

Simple TimeDistributed() wrapper Demo in Keras; sums images of MNIST digits

ZS219274/Planetly_assigment

Updated 4 minutes ago This is an assignment from Planetly. The data is taken from Kaggle, two datasets: GlobalLandTemperaturesByCity.csv and GlobalLandTemperaturesByCountry.csv . https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data. The dataset contains the observations of the monthly average temperature of cities and of countries. Comments: The data, especially City dataset is quite big and full. In all calculations I used a decadal average. The data is more or less clean, especially after the 1900 year. Both achieves have around 3-4% of missing data. This data I replaced by the mean value calculated over the period 1900-2013. The data was averaged from daily values initially. Obviously that data wasn't manually reconstructed before. This is good, since it gives us the opportunity to work more on the quality control of the data and provide and clean data more carefully. Data cleaning: Nan's have been replaced by the average calculated for the period 1900-2013. It was my "straightforward" approach since there was no time to go deep into this problem. However, with the extra time, I could find other temperature data archives and provide a comparison analysis between the data. If data has all necessary values it might be considered for being for replacing. Data can be: observational, modelling and reconstructional. Thus, we would have a diverse choice of where and which data to take. With the extra time, I would go more away from decadal averaging and do monthly scaling per year or per 11/30 years. During 20th century there've been small and relatively large climate seasons with similar weather conditions. Relatively large (again, I'm talking only on century-scale) last around 30 years, small = 11 year. Thus, I would rather think about doing 11 years averaging or 30 years instead of 10 years. According to climatology, there are certain climatological periods like: 1950-1980, 1981-2010. During those periods, a relatively similar climate was observed. With more time I would do an outlier analysis, this actually can also help to determine the best averaging periods. I would also spend more time on the visualisation part, with providing spatial distribution (maps) of temperature. I have split data into 70/30 ration for obtaining training and testing data. I didn't perform a cross-validation set, however, It worth doing. For the modelling approach I have taken LSTM since I worked only on 4 cities and it calculated relatively quick, with good performance (according to baseline model). I employed a very simple baseline model based on averaging. Of course, since the modelling part is the most interesting, I would probably try to grab other meteorological parameters and would create/generate more features. Then I would probably try some regression approaches (log, lasso) and XGBoosting Regressor. Of course, I would experiment on baseline models as well.

ZS219274/rcie-leaf

ZS219274/Rice-Leaf-Disease-Detection-using-Machine-learning

ZS219274/rice-leaf-diseases-detection

For this project, we are going to detect rice leaf disease using CNN and serve the result via messenger chatbot. We will also implement this to an independent Android app.

ZS219274/RiceDiseases-DataSet

Data Set for Rice Diseases with labels

ZS219274/riceleaf-dataset

riceleaf dataset

ZS219274/sylar

C++高性能分布式服务器框架,webserver,websocket server,自定义tcp_server（包含日志模块，配置模块，线程模块，协程模块，协程调度模块，io协程调度模块，hook模块，socket模块，bytearray序列化，http模块，TcpServer模块，Websocket模块，Https模块等, Smtp邮件模块, MySQL, SQLite3, ORM,Redis,Zookeeper)

ZS219274/TensorFlow-Deep-Learning

用TensorFlow搭建CNN/RNN/LSTM/GRU/BiRNN/BiLSTM/BiGRU/Capsule Network等deep learning模型

ZS219274/tensorflow-tutorial-samples

TensorFlow2教程 TensorFlow 2.0 Tutorial 入门教程实战案例

ZS219274/Text-Classification-pytorch

CNN BiGRU ensembled-method