https://github.com/yingding/tf-every-day
This ist a repository for learning series TensorFlow 2.x on every day.
https://github.com/yingding/tf-every-day
Last synced: 5 months ago
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This ist a repository for learning series TensorFlow 2.x on every day.
- Host: GitHub
- URL: https://github.com/yingding/tf-every-day
- Owner: yingding
- Created: 2021-03-01T10:09:23.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-12-27T14:21:45.000Z (over 1 year ago)
- Last Synced: 2025-09-05T13:52:25.243Z (11 months ago)
- Language: Jupyter Notebook
- Size: 19.1 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# tf-every-day
This is a repository for learning series TensorFlow 2.x on every day.
## Biography of Author
The author of this repository is a machine learning enthusiast, computer science researcher, fullstack and mobile developer.
## Goal
At the time of writing, the TensorFlow library has reached version 2.4.1. The way of writing code is fundamental different from tf version 1.5.x.
The author of this repository intends to add as much examples of how to use different models with TensorFlow version 2.x. Starting from using Regression models in Keras, loading csv files with Panda to build a [custom model with Keras](https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough) and define a [custom Keras Layer](https://www.tensorflow.org/tutorials/customization/custom_layers). It is not recommended to write model with low level tf 2.x api anymore, since the low level apis skip the model pipeline in [TFX](https://www.tensorflow.org/tfx/guide).
The author of this repository has limited time to contribute. If you have any question regarding TensorFlow 2.x, please post your question on [Stack Overflow - tensorflow2.x](https://stackoverflow.com/questions/tagged/tensorflow2.x).
Should the author of this repository has further free capacity, further tensorflow 2.x examples such as distributed training, federated learning or model training on GCP (Google Cloud Platform) might also be added. Time is always a limited factor in life besides family and work. Wish you a good time searching through the examples you might be interested in.
## Current Available Examples
* [01: Using Functional API to create a custom keras model](01CustomModelWithFunctionalAPI)
* [02: Build a muliple output model with keras](02BuildMultipleOutputModel)
* [03: Build a multiple input model to detect images similarity (Siamese ANN)](03MultipleInputsImageSimilarityModel)
## Additional Read for this Repository's Examples
Follow this [additional read](AdditionalReferences.md) to better understand the examples in this repository
## Useful TensorFlow References
* [Beginner guide TensorFlow](https://www.tensorflow.org/overview)
* [Advanced guild Keras subclassing](https://www.tensorflow.org/tutorials/quickstart/advanced)
* [Using Keras with TFX](https://www.tensorflow.org/tfx/guide/keras)
* [Train custom model with Keras: a walkthrough](https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough)
* [TensorBoard: TF's visualization toolkit](https://www.tensorflow.org/tensorboard)
## Book References:
* [Online Early Access: François Chollet, Deep Learning with Python, 2021, 2nd, Manning Publications](https://www.manning.com/books/deep-learning-with-python-second-edition), click on "Look Inside" to access the current MEAP.
* [Online Book: Michael Nielsen, Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) provides a theoretical background on neural networks in general. It doesn't use tensorflow, but it is a great reference to learn more fundamentals about neural networks.
## Online Labs:
* [QwikLabs - TensorFlow on GCP](https://www.qwiklabs.com/quests/83?catalog_rank=%7B%22rank%22%3A3%2C%22num_filters%22%3A0%2C%22has_search%22%3Atrue%7D&search_id=9114382) is an online Lab which works you trough a guided tutorial to use TensorFlow on Google Cloud Platform (GCP). You will do the Lab in a real GCP virtual machine in a time limited base. Unfortunately the QwikLab quests are not free of charge, but it total worth it to have a monthly subscription to go through some quest to gain knowledge of distributed training of tensorflow in GCP.
## Fun Online Sources:
* [TensorFlow Playground](https://playground.tensorflow.org/) helps to understand the fundamental of neural network through a interactive fun way.
* [Teachable Machine](https://teachablemachine.withgoogle.com/) is an online Application, allows you to train simple Vison and Audio Models with your web camera or mic very quickly. You can export the models and use them for any of your school projects.
## Advanced Science Reference:
* [HuggingFace Transformer for NLP](https://huggingface.co/transformers/index.html)
* [Building AutoEncoder in Keras](https://blog.keras.io/building-autoencoders-in-keras.html)
* [Time Series Forcasting with SARIMAX](https://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html)