The goal of this notebook is to implement and compare different approaches to predict item-level sales at different store locations.
-
Updated
Jan 11, 2022 - Jupyter Notebook
The goal of this notebook is to implement and compare different approaches to predict item-level sales at different store locations.
Deep Reinforcement Learning for Trading
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.
load point forecast
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.
VoiceVibes presents a solution for speech emotion recognition, featuring six innovative AI model architectures designed to accurately categorize emotional expressions conveyed through speech.
End-to-End Automatic Speech Recognition on PyTorch with CTC Decoder and Ken LM
realtime violence detection from videos using cnn-lstm model
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.
Hybrid Model with CNN and LSTM for VMD dataset using Python
PM2.5 aerosol prediction
Detect stress use EEG signal and Deep learning
In this project the next day's close price of the Fameli stock in TSE will be predicted using CNN-LSTM model
A DEEP-REINFORCEMENT LEARNING MODEL FOR SINGLE-FAULT SCENARIOS
Detecting affective states using CNN-LSTM in MMA Dataset
A DEEP-REINFORCEMENT LEARNING MODEL FOR SIMULTANEOUS SCENARIOS
A docker environment and notebooks to experiment with the extraction of moore machines from RNN RL policies
Add a description, image, and links to the cnn-lstm-models topic page so that developers can more easily learn about it.
To associate your repository with the cnn-lstm-models topic, visit your repo's landing page and select "manage topics."