Difference between revisions of "Google"
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** [[TensorBoard]] ...TensorFlow's visualization toolkit | ** [[TensorBoard]] ...TensorFlow's visualization toolkit | ||
** [http://www.tensorflow.org/hub 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. | ** [http://www.tensorflow.org/hub 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. | ||
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* [[Natural Language Processing (NLP)|Natural Language]]: | * [[Natural Language Processing (NLP)|Natural Language]]: | ||
** [http://github.com/pair-code/lit Language Interpretability Tool (LIT) | Google - GitHub] | ** [http://github.com/pair-code/lit Language Interpretability Tool (LIT) | Google - GitHub] | ||
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** [http://venturebeat.com/2019/03/12/gboard-on-pixel-phones-now-uses-an-on-device-neural-network-for-speech-input/ Gboard] on Pixel phones now uses an on-device neural network for speech recognition | Kyle Wiggers - VentureBeat | ** [http://venturebeat.com/2019/03/12/gboard-on-pixel-phones-now-uses-an-on-device-neural-network-for-speech-input/ Gboard] on Pixel phones now uses an on-device neural network for speech recognition | Kyle Wiggers - VentureBeat | ||
** [http://github.com/tensorflow/tfjs-models/tree/master/universal-sentence-encoder Universal Sentence Encoder lite] a model that encodes text into 512-dimensional embeddings. These embeddings can then be used as inputs to natural language processing tasks such as sentiment classification and textual similarity analysis. This module is a [[TensorFlow.js]] | ** [http://github.com/tensorflow/tfjs-models/tree/master/universal-sentence-encoder Universal Sentence Encoder lite] a model that encodes text into 512-dimensional embeddings. These embeddings can then be used as inputs to natural language processing tasks such as sentiment classification and textual similarity analysis. This module is a [[TensorFlow.js]] | ||
| + | * [[AI Platform]] | ||
| + | ** [http://cloud.google.com/blog/products/ai-machine-learning/all-ai-announcements-from-google-next19-the-smartest-laundry-list All 29 AI announcements from Google Next ‘19: the smartest laundry list] | ||
| + | ** [http://venturebeat.com/2019/04/10/google-launches-ai-platform-a-collaborative-model-making-tool-for-data-scientists/ Google launches AI Platform, a collaborative model-making tool for data scientists | Khari Johnson - VentureBeat] | ||
* [[AI Hub]] - explore and use a variety of AI asset categories. [[AI Hub]] offers a collection of components for developers and data scientists building artificial intelligence (AI) systems. Use [[AI Hub]] to: [1] Find, deploy, and use [[Kubeflow Pipelines]] and components, [2] explore code and learn in interactive [[Jupyter]] notebooks, [3] explore and reuse [[TensorFlow]] modules, Explore, deploy, and use trained models, [4] use prepackaged virtual machine (VM) images to quickly set up your AI environment, and [5] share AI components with your colleagues. | * [[AI Hub]] - explore and use a variety of AI asset categories. [[AI Hub]] offers a collection of components for developers and data scientists building artificial intelligence (AI) systems. Use [[AI Hub]] to: [1] Find, deploy, and use [[Kubeflow Pipelines]] and components, [2] explore code and learn in interactive [[Jupyter]] notebooks, [3] explore and reuse [[TensorFlow]] modules, Explore, deploy, and use trained models, [4] use prepackaged virtual machine (VM) images to quickly set up your AI environment, and [5] share AI components with your colleagues. | ||
* [[Kubeflow Pipelines]] ML workflows on Kubernetes | * [[Kubeflow Pipelines]] ML workflows on Kubernetes | ||
Revision as of 10:11, 13 September 2020
YouTube search... ...Google search
- Tools and Resources
- Platforms: Machine Learning as a Service (MLaaS)
- Colaboratory (Colab) - Jupyter notebooks
- Hands-on labs
- TensorFlow
- TensorBoard ...TensorFlow's visualization toolkit
- 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.
- Natural Language:
- Language Interpretability Tool (LIT) | Google - GitHub
- Google Natural Language
- Google Semantic Reactor
- Bidirectional Encoder Representations from Transformers (BERT)
- Gboard on Pixel phones now uses an on-device neural network for speech recognition | Kyle Wiggers - VentureBeat
- Universal Sentence Encoder lite a model that encodes text into 512-dimensional embeddings. These embeddings can then be used as inputs to natural language processing tasks such as sentiment classification and textual similarity analysis. This module is a TensorFlow.js
- AI Platform
- AI Hub - explore and use a variety of AI asset categories. AI Hub offers a collection of components for developers and data scientists building artificial intelligence (AI) systems. Use AI Hub to: [1] Find, deploy, and use Kubeflow Pipelines and components, [2] explore code and learn in interactive Jupyter notebooks, [3] explore and reuse TensorFlow modules, Explore, deploy, and use trained models, [4] use prepackaged virtual machine (VM) images to quickly set up your AI environment, and [5] share AI components with your colleagues.
- Kubeflow Pipelines ML workflows on Kubernetes
- Google AutoML
- Cloud Vision
- Cloud Vision API - drag & drop picture on webpage
- Automated Machine Learning (AML) - AutoML
- We’re making tools and resources available so that anyone can use technology to solve problems | Google AI
- Dopamine - reinforcement learning algorithms
- Google AI Experiments
- ML Engine
- Prediction API
- Grow with Google
- Learn from ML experts at Google
- Google Internet of Things (IoT)
- Psychlab ...testing virtual agents in 3D environments
- Deep Reinforcement Learning (DRL) ...Importance Weighted Actor-Learner Architecture (IMPALA)
- Google AIY Projects Program ... connect Vision or Voice Kit to a Wi-Fi network right from mobile device
- Android Neural Networks API ...machine learning on Android devices
- Big\Table ...designed with a storage engine for machine learning applications leading to better predictions
- Pub/Sub ...messaging and ingestion for event-driven systems and streaming analytics.
- BiqQuery ML (BQML) ...enables users to create and execute machine learning models in BigQuery by using standard SQL queries.
- Firebase ...mobile and web functionality like analytics, databases, messaging and crash reporting
- DialogFlow ...creating conversational AI applications, including chatbots, voicebots, and IVR bots
- Google Analytics ...measure your web traffic
- Teachable Machine ...train a computer to recognize your own images, sounds, & poses
- Google Coral ...toolkit to build products with local AI. Our on-device inferencing capabilities
- Google Sheets ...access, create, and edit your spreadsheets wherever you go — from your phone, tablet, or computer
- Google Facets ...contains two robust visualizations to aid in understanding and analyzing machine learning datasets
- Google Cloud
- Google Cloud Code and Google Kubernetes Engine (GKE)
- DeepMind - Open Source
- People + AI Research (PAIR) ... devoted to advancing the research and design of people-centric AI systems.
The Path From Cloud AutoML to Custom Model (Cloud Next '19)
by Sara Robinson
