A DEEP-REINFORCEMENT LEARNING MODEL FOR SIMULTANEOUS SCENARIOS
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Updated
Mar 11, 2024 - Jupyter Notebook
A DEEP-REINFORCEMENT LEARNING MODEL FOR SIMULTANEOUS SCENARIOS
In this project the next day's close price of the Fameli stock in TSE will be predicted using CNN-LSTM model
A docker environment and notebooks to experiment with the extraction of moore machines from RNN RL policies
A DEEP-REINFORCEMENT LEARNING MODEL FOR SINGLE-FAULT SCENARIOS
Detecting affective states using CNN-LSTM in MMA Dataset
The Sea Ice Extent of 5 Arctic and Antarctic regions is forecasted using CNN+LSTM, Bidirectional LSTM and Standalone LSTM.
This project uses CNN and CNN-LSTM models to classify ADHD from fMRI data, using pretrained weights from a CNN autoencoder (CNN-AE) for better feature extraction.
PM2.5 aerosol prediction
Detect stress use EEG signal and Deep learning
A deep learning model that predicts the demand of an item for a particular time period in 10 retail stores. The model showed an RMSE of 18. Various deep learning models such as CNN, LSTM, MLP, CNN-LSTM were compared and CNN-LSTM showed the least RMSE.
realtime violence detection from videos using cnn-lstm model
Hybrid Model with CNN and LSTM for VMD dataset using Python
VoiceVibes presents a solution for speech emotion recognition, featuring six innovative AI model architectures designed to accurately categorize emotional expressions conveyed through speech.
Real-Time ASR with CNN-BiLSTM: End-to-End Live Streaming in Lightning AI ⚡ with Training Scripts
The goal is to learn to generate the Scalable Vector Graphics (SVG) code correspondig to images of simple colored shapes. SVG is a markup language which is used to define vector graphics.
load point forecast
The project is a concoction of research (audio signal processing, keyword spotting, ASR), development (audio data processing, deep neural network training, evaluation) and deployment (building model artifacts, web app development, docker, cloud PaaS) by integrating CI/CD pipelines with automated tests and releases.
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