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Expand Up @@ -9,39 +9,39 @@ I will try to make the tutorials accessible for everyone, thus I will try to wor
### List of tutorials available:
#### Getting started
---
* **01. Build a simple neural network** - This tutorial shows you how to build and train a one-hidden layer neural network using the Eager mode of TensorFlow, on a synthetically generated dataset.
* [**01. Build a simple neural network**](01_simple_feedforward_neural_network.ipynb) - This tutorial shows you how to build and train a one-hidden layer neural network using the Eager mode of TensorFlow, on a synthetically generated dataset.
<img src="tutorials_graphics/01_flowchart.png" style="display:block; margin-left: auto; margin-right: auto; width: 80%;"/>

* **02. Using metrics in Eager mode** - This tutorial shows you how to use metrics
* [**02. Using metrics in Eager mode**](02_using_metrics_in_eager_mode.ipynb) - This tutorial shows you how to use metrics
that are compatible with Eager mode, for three types of machine learning problems (multi-classification, imbalanced dataset and regression).
<img src="tutorials_graphics/02_flowchart.png" style="display:block; margin-left: auto; margin-right: auto; width: 80%;"/>

#### Simple but useful stuff
---
* **03. Save and restore a trained model** - Simple tutorial on how you can save a trained model and restore it at a later time to make predictions on new data.
* [**03. Save and restore a trained model**](03_save_and_restore_model.ipynb) - Simple tutorial on how you can save a trained model and restore it at a later time to make predictions on new data.
<img src="tutorials_graphics/save_restore_model.png" style="display:block; margin-left: auto; margin-right: auto; width: 80%;"/>

* **04. Transfer text data to TFRecords** - This tutorial shows you how to store text data of variable sequence length to TFRecords. The data can be easily padded
* [**04. Transfer text data to TFRecords**](04_text_data_to_tfrecords.ipynb) - This tutorial shows you how to store text data of variable sequence length to TFRecords. The data can be easily padded
on the fly, within a batch, when reading the dataset with an iterator.
<img src="tutorials_graphics/text2tfrecords.png" style="display:block; margin-left: auto; margin-right: auto; width: 80%;"/>

* **05. Transfer image data to TFRecords** - Easy and simple tutorial on how to transfew image data and its metadata (e.g. target) to TFRecords.
* [**05. Transfer image data to TFRecords**](05_images_to_tfrecords.ipynb) - Easy and simple tutorial on how to transfew image data and its metadata (e.g. target) to TFRecords.
<img src="tutorials_graphics/images2tfrecords.png" style="display:block; margin-left: auto; margin-right: auto; width: 80%;"/>

* **06. How to read TFRecords data in batches** - This tutorial shows you how to read either variable length sequence data or image data, in batches, from TFRecords.
* [**06. How to read TFRecords data in batches**](06_read_data_in_batches_from_tfrecords.ipynb) - This tutorial shows you how to read either variable length sequence data or image data, in batches, from TFRecords.
<img src="tutorials_graphics/readbatches.png" style="display:block; margin-left: auto; margin-right: auto; width: 80%;"/>

#### Convolutional neural networks
----
* **07. Build a CNN for emotion recognition** - This tutorial shows you how to build a CNN from scratch using the TensorFlow Eager API and the FER2013 dataset. At the end of the tutorial you will be able to test the network on yourself using a webcam. Very fun exercise!
* [**07. Build a CNN for emotion recognition**](07_convolutional_neural_networks_for_emotion_recognition.ipynb ) - This tutorial shows you how to build a CNN from scratch using the TensorFlow Eager API and the FER2013 dataset. At the end of the tutorial you will be able to test the network on yourself using a webcam. Very fun exercise!
<img src="tutorials_graphics/03_flowchart.png" style="display:block; margin-left: auto; margin-right: auto; width: 80%;"/>

#### Recurrent neural networks
----
* **08. Build a dynamic RNN for sequence classification** - Learn how to work with variable sequence input data. This tutorial shows you how to build a dynamic RNN using the TensorFlow Eager API and the Stanford Large Movie Review Dataset.
* [**08. Build a dynamic RNN for sequence classification**](08_dynamic_recurrent_neural_networks_for_sequence_classification.ipynb) - Learn how to work with variable sequence input data. This tutorial shows you how to build a dynamic RNN using the TensorFlow Eager API and the Stanford Large Movie Review Dataset.
<img src="tutorials_graphics/04_flowchart.png" style="display:block; margin-left: auto; margin-right: auto; width: 80%;"/>

* **09. Build a RNN for time series regression** - Learn how to build a RNN for timeseries forecasting.
* [**09. Build a RNN for time series regression**](09_recurrent_neural_networks_for_time_series_regression.ipynb) - Learn how to build a RNN for timeseries forecasting.
<img src="tutorials_graphics/05_flowchart.png" style="display:block; margin-left: auto; margin-right: auto; width: 80%;"/>


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