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https://github.com/ivanbongiorni/tensorflow2.0_notebooks

Implementation of a series of Neural Network architectures in TensorFow 2.0
https://github.com/ivanbongiorni/tensorflow2.0_notebooks

autoencoder autograph batch-gradient-descent classifier cnn-classifier convolutional-neural-networks data-science deep-learning dimensionality-reduction forecast-model lstm machine-learning neural-network python python-3 rnn rnn-tensorflow tensorflow tensorflow-tutorials tensorflow2

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Implementation of a series of Neural Network architectures in TensorFow 2.0

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README

        

Author: Ivan Bongiorni, Data Scientist at GfK; [LinkedIn](https://www.linkedin.com/in/ivan-bongiorni-b8a583164/).

# TensorFlow 2.0 Notebooks

This is a collection of my Notebooks on TensorFlow 2.0

The training of models is based on TensorFlow's **eager execution** method. I'll try to minimize referencese to Keras.
## Summary of Contents:
- Basic feed forward stuff
- Autoencoders
- Convolutional Neural Networks
- Recurrent Neural Networks
- Applications to NLP

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## Contents:

**Basic feed forward stuff**:

1. [Basic classifier](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__00.01_basic_Classifier.ipynb): implementation of a **feed forward Classifier** with simple, full-Batch Gradient Descent in **Eager execution**.

2. [Mini batch gradient descent](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__00.02_MiniBatch_Gradient_Descent.ipynb): training a model with **Mini Batch Gradient Descent**.

3. [Save and restore models](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__00.03_Save_and_Restore_models.ipynb): how to train a model, save it, then restore it and keep training.

0. Train a Neural Network with frozen layers

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**Autoencoders**:

1. [Autoencoder for dimensionality reduction](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__02.01_Autoencoder_for_Dimensionality_Reduction.ipynb): implementation of a stacked **Autoencoder for dimensionality reduction** of datasets.

2. Denoising Autoencoder (see CNN section below).

0. Recurrent Autoencoder (see RNN section below).

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**Convolutional Neural Networks**:

1. [Basic CNN classifier](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__03.01_Convolutional_Neural_Network.ipynb): a basic **Convolutional Neural Network** for multiclass classification.

2. Advanced CNN classifier with custom data augmentation.

3. Mixed-CNN classifier.

4. Denoising Autoencoder.

---

**Recurrent Neural Networks**:

1. [LSTM many-to-one forecast model](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__04.01_RNN_many2one.ipynb)

2. [LSTM many-to-many forecast model](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__04.02_RNN_many2many.ipynb)

3. [Multivariate LSTM regression](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2__04.03_RNN_multivariate_regression.ipynb).

0. Seq2seq models.

---

**RNN + Natural Language Processing**

1. LSTM [Text generator](https://github.com/IvanBongiorni/TensorFlow2-RNN_text_generator-Dante_DivineComedy/blob/master/RNN_text_generator_00.ipynb) from [this repository of mine](https://github.com/IvanBongiorni/TensorFlow2-RNN_text_generator-Dante_DivineComedy).