bobo-zz

bobo-zz's Stars

• Snailclimb/JavaGuide

  「Java学习+面试指南」一份涵盖大部分 Java 程序员所需要掌握的核心知识。准备 Java 面试，首选 JavaGuide！

  Language:Java148k4.5k1.1k45.7k

• Alro10/deep-learning-time-series

  List of papers, code and experiments using deep learning for time series forecasting

  Language:Jupyter Notebook2.6k817529

• shamangary/SSR-Net

  [IJCAI18] SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation

  Language:Python5861951146

• CyberZHG/keras-multi-head

  A wrapper layer for stacking layers horizontally

  Language:Python22891153

• bojone/on-lstm

  Keras implement of ON-LSTM (Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks)

  Language:Python1559325

• wushidonguc/two-stream-action-recognition-keras

  Two-stream CNNs for video action recognition implemented in Keras

  Language:Python12291446

• dd1github/DeepSMOTE

  Pytorch implementation of "DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data".

  Language:Python11231030

• Makin-Things/bom-radar-card

  A rain radar card using the new tiled images from the Australian BOM

  Language:TypeScript100163112

• luochuyao/IDA_LSTM

  The Radar Echo Extrapolation Model

  Language:Python30252

• CyberZHG/keras-drop-block

  DropBlock implemented in Keras

  Language:Python264415

• sayakpaul/Revisiting-Pooling-in-CNNs

  Implements RNNPool and SoftPool for CNNs.

  Language:Jupyter Notebook14211

• iantimmis/DropBlock-Keras-Implementation

  Paper Reproduction: "DropBlock: A regularization method for convolutional networks"

  Language:Jupyter Notebook11406

• nastiag67/ecgn

  Concepts used: kNN, SVM, boosting (XGBoost, Gradient boosting, Light GBM, AdaBoost, Random Forests), deep learning (CNN, LSTM), ensembles (model stacking), transfer learning.

  11144

• cpuimage/DualAttentionGuidedDropout

  Unofficial Tensorflow Implementation of Dual-attention Guided Dropblock Module https://arxiv.org/abs/2003.04719

  Language:Python10301

• meteoswiss-mdr/rainforest

  A python library to compute QPE with random forest and for gauge/radar database management

  Language:Python7204

• ncu-dart/AR-LSTMs

  Predicting Transportation Demand based on AR-LSTMs Model with Multi-Head Attention

  Language:Python6202

• TonySl/Radar_Rainforest

  Code and data for generating long-term C-band radar data set for global tropical rainforests.

  Language:MATLAB50

• Jaewoong-Lee/cikm2017

  Language:Python4000

• TanyaChutani/Xception-Tf2.0

  A TensorFlow2.0 implementation of Xception Deep Learning with Depthwise Separable Convolutions

  Language:Jupyter Notebook4402

• mcdonaldabdullah/Comparison-of-Predictive-Models-for-Rainfall-Prediction-using-Big-Data-Technologies

  Rainfall is a form of precipitation and is responsible for providing most of the freshwater for animals and plants. Machine learning can be used to analyze data trends to develop a model. Deep learning on the other hand focuses more on using images specifically to analyze data. Trying to understand the patterns of rainfall to predict it has proven to be a difficult undertaking, as seen by the various research using machine learning and deep learning for this problem. When implementing a solution to this rainfall prediction problem, a vast amount of computational resources are usually required to execute it. Thus arises a need to properly store and analyze the data to effectively approach the prediction aspect. This paper investigated the comparison of predictive models for rainfall prediction using big data technologies and radar rainfall images. The literature on state-of-the-art prediction models was investigated and compared to survey which models could achieve satisfactory prediction results in combination with big data technologies. The models chosen were Random Forest Regressor and Deep LSTM and were used to predict 1,2, and 3 days ahead using monthly rainfall data. Results from this study showed that the Deep LSTM model performed better than the Random Forest Model for sequence lengths of 4, 8, and 12 when predicting 1, 2, and 3 months ahead.

  Language:Jupyter Notebook3100

• mcpavan/BiLSTM_MultiHeadAttention

  This repository contains a first version of a Recurrent Neural network for text classification based on Bidirectional Long-Short Term Memory Networks.

  Language:Jupyter Notebook3101

• collinthornton/RadarAtmosphericAttenuation

  Java code to calculate the atmospheric attenuation of radar signals due to FSL, refraction, rain, and cloud cover.

  Language:HTML2100

• inoue0406/CIKM2017

  Scripts for CIKM Analyticup 2017

  Language:R2200

• ml-boringtao/rnn-workshop

  Xception LSTM

  Language:Jupyter Notebook2000

• mwoodson1/temporal-pooling-networks

  Code for my entry in the Youtube8M Kaggle competition. Currently exploring applications to other video based problems

  Language:Python2201

• Tony-Xiang-Cao/MarsRover_EnergyPrediction_XceptionCNN_LSTM

  A Xception-CNN-TimeDistributed Model for predicting Mars Rover energy consumption based on

  Language:Jupyter Notebook2100

• iliguangwei/CIKM2017

  Play Hard

  Language:Python110

• karthajee/minivggnet

  An implementation of a MiniVGGNet ([1] 2 blocks of CONV => ACT => BN => CONV => ACT => BN connected via POOL => DROPOUT, [2] 3x3 filters throughout and [3] FC layers connected by DROPOUT) in tensorflow.keras

  Language:Jupyter Notebook10

• salinasJJ/BBaction

  Video Action Recognition/Classification using Channel-Separated Convolutional Networks. Implemented in Tensorflow 2.

  Language:Python1101

• tanreinama/XceptionHourgrass---PyTorch

  Stacked Hourgrass Network using Xception Blocks.

  Language:Python120