https://github.com/peremartra/tensorflow_advanced
https://github.com/peremartra/tensorflow_advanced
Last synced: 21 days ago
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- Host: GitHub
- URL: https://github.com/peremartra/tensorflow_advanced
- Owner: peremartra
- License: mit
- Created: 2022-10-06T09:49:12.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-25T15:13:04.000Z (about 2 years ago)
- Last Synced: 2025-02-02T08:27:03.220Z (8 months ago)
- Language: Jupyter Notebook
- Size: 117 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# TensorFlow Advanced Techniques
It is a series of notebooks about some “beyond the basics” or mid-advanced techniques with TensorFlow and Keras.
It could be said that these notebooks pick up where the TensorFlow Developer Certified exam leaves off.
It is not a course or tutoria, just a colletion of notebooks and articles explaining some interesting techniques that you can use with TensorFlow and Keras.
## Improve TensorFlow performance with Graps Mode.
Using the *cats_vs_dogs* dataset we are going to explore hot to use the Graph Mode and how it can improve the performance in our Notebooks.
* Notebook: [graph-mode-vs-eager-mode-in-tensorflow.ipynb](https://github.com/peremartra/TensorFlow_Advanced/blob/main/graph-mode-vs-eager-mode-in-tensorflow.ipynb)
* Article: [Improve the Performance Easily in TensorFlow Using Graph Mode](https://medium.com/towards-artificial-intelligence/improve-the-performance-easily-in-tensorflow-using-graph-mode-288ee35dddae)
## Multiple Outputs with Keras Functional API.
Using the functional API and a non sequential Model we are going to learn ho to create a model able to predict more than one variable. The Datased used is the *Wine_quality* and we will predict a Classification Variable to classify the wine, and a Regression variable to qualify the wine.
* Notebook: [guide-multiple-outputs-with-keras-functional-api.ipynb](https://github.com/peremartra/TensorFlow_Advanced/blob/main/guide-multiple-outputs-with-keras-functional-api.ipynb)
* Article: [How To Predict Multiple Variables With One Model? And Why!](https://medium.com/towards-artificial-intelligence/how-to-predict-multiple-variables-with-one-model-and-why-31e6a8efc09e)
## How to create a siamese network with Tensorflow.
we are going to build a Siamese Model with Tensorflow able to compare images and return a difference between them, identifying when the images are of the same type.
* Notebook: [how-to-create-a-siamese-network-to-compare-images.ipynb](https://github.com/peremartra/TensorFlow_Advanced/blob/main/how-to-create-a-siamese-network-to-compare-images.ipynb)
* Article: [How To Create a Siamese Network With Keras to Compare Images](https://medium.com/towards-artificial-intelligence/how-to-create-a-siamese-network-with-keras-to-compare-images-5713b3ee7a28)
## Using Multiple GPUs with TensorFlow on Kaggle.
Recently, Kaggle has introduced the possibility of using two GPUs in our notebooks. We are going to see how to use them in a simple way with TensorFlow.
The technique of using more than one GPU on a single machine is called MirroredStrategy. It is the one we are going to use in Kaggle. Or on our machine if we had more than one GPU.
* Notebook: [using-multiple-gpu-s-with-keras-on-kaggle.ipynb](https://github.com/peremartra/TensorFlow_Advanced/blob/main/using-multiple-gpu-s-with-keras-on-kaggle.ipynb)
By the moment, that's all!
If you want to continue your learning path, I recommend you my [repository about GANs](https://github.com/peremartra/GANs) with 4 notebooks organized as an Introductory course to GenerativeAI.