RNN
01 RNN_Classification
Simple RNN training for classification task of 3 signal: Sine, Square, Triangle.
02 RNN_Regression
Simple RNN training for sine wave estimation.
03 RNN_vs_GRU_Classification
Comparison of RNN model and GRU model for classification task of 3 signal: Sine, Square and Triangle, after 100 epoch training.
Model  Accuracy 

RNN Model  0.9315 
GRU Model  0.9383 
04 RNN_vs_GRU_Regression
Comparison of RNN model and GRU model for regression task of sine wave estimation after 100 epoch training.
Model  loss 

RNN Model  0.0027 
GRU Model  0.0026 
05 Ball_Move_Data_Generation
Generate data for ball move direction
06 GRU_Implementation_from_Scratch
GRU implementation from scratch + inference
07LSTM_Implementation_from_Scrat
LSTM implementation from scratch + inference
08 Ball_Move_Direction_Classification

Generate data for ball move direction

Classification of direction using RNN, GRU and LSTM
09 VideoClassificationCRNN
 09 Video_Classification_CRNN.ipynb(train)
 inference.py
 models.py (gru, lstm, rnn)
 load_video.py
 requirements.txt
Model
Backbone: ResNet50V2
and my vgg base model
for feature extraction
RNN modules: RNN, GRU and LSTM are tested
The performance of GRU module was better than other madules
Dataset
Dataset contains videos from 2 classes
Due to insufficient data, the training was not done well. but this project can be used for other video classification tasks using CRNNs
10 Video_Classification_CRNN
 Video classificatio nusing CRNN on ucf101_top5 dataset
Model
Backbone: my vgg base model
for feature extraction
RNN modules: RNN, GRU are tested
The performance of GRU module was better than RNN madules
Dataset
 ucf101_top5 dataset containing 573 video from 5 classes
Result
Model  Val Accuracy 

RNN Model  0.87 
GRU Model  0.94 