Difference between revisions of "TensorFlow"
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[https://www.bing.com/news/search?q=TensorFlow&qft=interval%3d%228%22 ...Bing News] | [https://www.bing.com/news/search?q=TensorFlow&qft=interval%3d%228%22 ...Bing News] | ||
| − | * [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[ | + | * [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with JavaScript|GenAI w/ JavaScript]] ... [[TensorFlow]] ... [[PyTorch]] |
* [[Google]] offerings | * [[Google]] offerings | ||
* [[Libraries & Frameworks Overview]] ... [[Libraries & Frameworks]] ... [[Git - GitHub and GitLab]] ... [[Other Coding options]] | * [[Libraries & Frameworks Overview]] ... [[Libraries & Frameworks]] ... [[Git - GitHub and GitLab]] ... [[Other Coding options]] | ||
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* [[Keras]] (currently part of TensorFlow 2.0) | * [[Keras]] (currently part of TensorFlow 2.0) | ||
* [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]] | * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]] | ||
| − | * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless | + | * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]] |
| − | * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] | + | * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]] |
* [http://venturebeat.com/2019/09/30/google-launches-tensorflow-2-0-with-tighter-keras-integration/ Google launches TensorFlow 2.0 with tighter Keras integration | Khari Johnson] | * [http://venturebeat.com/2019/09/30/google-launches-tensorflow-2-0-with-tighter-keras-integration/ Google launches TensorFlow 2.0 with tighter Keras integration | Khari Johnson] | ||
* [[TensorFlow.js]] ... Browser and Node Server | * [[TensorFlow.js]] ... Browser and Node Server | ||
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| − | <img src="https://content.altexsoft.com/media/2017/08/tensors_flowing-1.gif" width=" | + | <img src="https://content.altexsoft.com/media/2017/08/tensors_flowing-1.gif" width="400"> |
Latest revision as of 12:42, 6 November 2024
YouTube ... Quora ...Google search ...Google News ...Bing News
- Python ... GenAI w/ Python ... JavaScript ... GenAI w/ JavaScript ... TensorFlow ... PyTorch
- Google offerings
- Libraries & Frameworks Overview ... Libraries & Frameworks ... Git - GitHub and GitLab ... Other Coding options
- TensorFlow is a Python library
- Keras (currently part of TensorFlow 2.0)
- Analytics ... Visualization ... Graphical Tools ... Diagrams & Business Analysis ... Requirements ... Loop ... Bayes ... Network Pattern
- Development ... Notebooks ... AI Pair Programming ... Codeless ... Hugging Face ... AIOps/MLOps ... AIaaS/MLaaS
- Gaming ... Game-Based Learning (GBL) ... Security ... Generative AI ... Games - Metaverse ... Quantum ... Game Theory ... Design
- Google launches TensorFlow 2.0 with tighter Keras integration | Khari Johnson
- 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
- TensorFlow Federated
- TensorFlow Extended (TFX) an end-to-end platform for deploying production ML pipelines
- TensorFlow Playground
- TensorBoard with Embedding Projector - visualization of high-dimensional data
- We’re making tools and resources available so that anyone can use technology to solve problems | Google AI
- 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
- TensorFlow Hub is a library for reusable machine learning modules that you can use to speed up the process of training your model. A TensorFlow module is a reusable piece of a TensorFlow graph. With transfer learning, you can use TensorFlow modules to preprocess input feature vectors, or you can incorporate a TensorFlow module into your model as a trainable layer. This can help you train your model faster, using a smaller dataset, while improving generalization.
- Neural Structured Learning (NSL) in TensorFlow
- Git - GitHub and GitLab
- 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...
- Best Machine Learning Tools: Experts’ Top Picks | Altexsoft
- What's new in TensorFlow 2.13 and Keras 2.13? | TensorFlow & Keras Teams
- Coding TensorFlow <--- video series
TensorFlow 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
Contents
[hide]GPU
tf.function
Eager Execution (Default in 2.0) =
- Eager Execution vs. Graph Execution in TensorFlow: Which is Better? | Orhan G. Yalcin ...Comparing Eager Execution and Graph Execution using Code Examples, Understanding When to Use Each and why TensorFlow switched to Eager Execution | Deep Learning with TensorFlow 2.x
tf.data - TF Input Pipeline
tf.distribute
mesh-TensorFlow
TensorFlow Probability
Reinforcement Learning with TF-Agents
Examples
Tensorflow for Practice Specialization
- Chapter 1:
- Presentation
- Meetup #1 fun-packed event
- Notes:
- Why map the pixel grayscale [0,1 to [0.01, 0.99] before feeding to the neural network?]
- How to Normalize, Center, and Standardize Image Pixels in Keras | Jason Brownlee
