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https://github.com/machinelearningzuu/tf2-gradienttape-examples

This Repository Contains examples for Tensorflow 2.x Eager execution (Gradient Tape) techniques and examples contain ANN, CNN, RNN models with entire Tensorflow ETL pipeline
https://github.com/machinelearningzuu/tf2-gradienttape-examples

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This Repository Contains examples for Tensorflow 2.x Eager execution (Gradient Tape) techniques and examples contain ANN, CNN, RNN models with entire Tensorflow ETL pipeline

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# Tensorflow 2 GradientTape Examples

This Repository Contains examples for Tensorflow 2.x Eager execution (GradientTape) techniques and examples contain ANN, CNN, RNN models with entire Tensorflow ETL pipeline. Using Eager Executions it will be able to generate dynamic graphs instead of static graphs like in tensorflow older versions

In tensorflow older version first you need to create the entire static graph and then execute under defined session using placeholders.

# Techniques

- Supervised Deep Learning, Unsupervised Deep Learning
- Natural language processing
- Computer vision
# Tools

* TensorFlow - Deep Learning Model
* pandas - Data Extraction and Preprocessing
* numpy - numerical computations
* scikit learn - Advanced preprocessing

### Installation

Install the dependencies and conda environment

```sh
$ conda create -n envname python=python_version
$ activate envname
$ conda install -c anaconda tensorflow-gpu
$ conda install -c anaconda pandas
$ conda install -c anaconda matplotlib
$ conda install -c anaconda scikit-learn
```