Difference between revisions of "TensorFlow"
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* [http://github.com/machinelearningmindset/TensorFlow-Course TensorFlow-Course | GitHub] | * [http://github.com/machinelearningmindset/TensorFlow-Course TensorFlow-Course | GitHub] | ||
* [[Git - GitHub and GitLab]] | * [[Git - GitHub and GitLab]] | ||
| + | * [[Python]] | ||
* The API... | * The API... | ||
** [http://www.tensorflow.org/guide/estimators tf.estimator] available alongside the newer Keras high-level API. | ** [http://www.tensorflow.org/guide/estimators tf.estimator] available alongside the newer Keras high-level API. | ||
Revision as of 07:37, 20 July 2019
Youtube search... ...Google search
- Libraries & Frameworks
- TensorFlow 2.0 Alpha
- TensorFlow.js ... Browser and Node Server
- TensorFlow Serving .. Cloud, On-prem ...support model versioning (for model updates with a rollback option) and multiple models (for experimentation via A/B testing)
- TensorFlow Lite ... Android, iOS, Raspberry Pi
- Converting to TensorFlow Lite convert models into TensorFlow Lite format
- TensorBoard
- TensorFlow Federated
- TensorFlow Extended (TFX) an end-to-end platform for deploying production ML pipelines
- Data Validation (TFDV) helps developers understand, validate, and monitor their ML data
- Model Analysis (TFMA) enables developers to compute and visualize evaluation metrics for their models
- TensorFlow Playground
- We’re making tools and resources available so that anyone can use technology to solve problems | Google AI
- Keras
- TensorFlow Workshops click on links to run in Colaboratory (Colab)
- Machine Learning Crash Course with TensorFlow APIs | Google
- TensorFlow without a PhD | Martin Görner
- Running Tensorflow in Production | Matthias Feys
- Simple Tensorflow Cookbook
- TensorFlow-Course | GitHub
- Git - GitHub and GitLab
- Python
- The API...
- tf.estimator available alongside the newer Keras high-level API.
- tf.function a wrapper to use when writing certain functions in Python]
- tf.Transform includes converting between formats, tokenizing and stemming text and forming vocabularies
- Related...
- Coding TensorFlow <--- video series
TensorFlow 2.0
TensorFlow 2.0 focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs, and flexible model building on any platform, tight Keras integration. You can easily ingest datasets via tf.data pipelines, and you can monitor your training in TensorBoard directly from Colaboratory and Jupyter Notebooks. TensorFlow 2.0 and Google Cloud AI make it easy to train, deploy, and maintain scalable machine learning models | Paige Bailey and Barrett Williams - Google
- What’s coming in TensorFlow 2.0 | TensorFlow Team - Medium
- What’s in store for ML developers in TensorFlow 2.0? | Jane Elizabeth
TensorFlow 1.0
Eager Execution (Default in 2.0)
