TensorFlow implementations of several deep learning models (e.g. variational autoencoder, RNN, ...)
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Updated
Jun 26, 2018 - Jupyter Notebook
TensorFlow implementations of several deep learning models (e.g. variational autoencoder, RNN, ...)
A set of notebooks that explores the power of Recurrent Neural Networks (RNNs), with a focus on LSTM, BiLSTM, seq2seq, and Attention.
Jupyter notebooks implementing Deep Learning algorithms in Keras and Tensorflow
In this notebook, I built machine learning and neural network models to regress and predict Rossmann stores' daily sales.
Repository contains my Jupyter Notebook files (ran either in VSCode using the Jupyter Notebook extension, either Notebook or Lab through Anaconda, or Google Colab) for a Recurrent Neural Network (RNN) regressor model that predicts energy demand in t-horizon, for EEL6812 - Advanced Topics in Neural Networks (Deep Learning with Python) course, PRJ03
This notebook loads a time series of gas concentrations in the air to train a recurrent neural network to predict the next hour of data
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