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1. Deep-Learning-For-Hackers Deep-Learning-For-Hackers Public

   Forked from curiousily/Deep-Learning-For-Hackers

   Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencode…

   Jupyter Notebook

2. Multivariate-Time-Series-forecast-using-seq2seq-in-TensorFlow Multivariate-Time-Series-forecast-using-seq2seq-in-TensorFlow Public

   Forked from aaxwaz/Multivariate-Time-Series-forecast-using-seq2seq-in-TensorFlow

   Jupyter Notebook

3. New-York-Stock-Exchange-Predictions-RNN-LSTM New-York-Stock-Exchange-Predictions-RNN-LSTM Public

   Forked from deadskull7/New-York-Stock-Exchange-Predictions-RNN-LSTM

   BEST SCORE ON KAGGLE SO FAR. Mean Square Error after repeated tuning 0.00032. Used stacked GRU + LSTM layers with optimized architecture, learning rate and batch size for best model performance. Th…

   Jupyter Notebook

4. RNN_LSTM_trading_model RNN_LSTM_trading_model Public

   Forked from jiewwantan/RNN_LSTM_trading_model

   This project explores stock trading modelling with the use recurrent neural network (RNN) with long-short term memory (LSTM) architecture. This is for single stock prediction and backtesting, anoth…

   Jupyter Notebook

5. Sell-Qty-Prediction-over-time-series-data-using-GRUs Sell-Qty-Prediction-over-time-series-data-using-GRUs Public

   Forked from sudip-padhye/Sell-Qty-Prediction-over-time-series-data-using-GRUs

   Time-series analysis of qty sale using Gated Recurrent Units

   Jupyter Notebook

6. Time-Series-Demand-Forecasting Time-Series-Demand-Forecasting Public

   Forked from takumiw/Time-Series-Demand-Forecasting

   Time-series demand forecasting is constructed by using LSTM, GRU, LSTM with seq2seq architecture, and prophet models.

   Jupyter Notebook 1