Difference between revisions of "Google"
m |
m |
||
| Line 10: | Line 10: | ||
* [[Platforms: Machine Learning as a Service (MLaaS)]] | * [[Platforms: Machine Learning as a Service (MLaaS)]] | ||
* [[Colaboratory]] (Colab) - [[Jupyter]] notebooks | * [[Colaboratory]] (Colab) - [[Jupyter]] notebooks | ||
| − | * [http://codelabs.developers.google.com/ Google Developers Codelabs] | + | ** [http://codelabs.developers.google.com/ Google Developers Codelabs] |
* [[TensorFlow]] | * [[TensorFlow]] | ||
* [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. | ||
| Line 31: | Line 31: | ||
* [[Kubeflow Pipelines]] ML workflows on Kubernetes | * [[Kubeflow Pipelines]] ML workflows on Kubernetes | ||
* [[Google Cloud]] | * [[Google Cloud]] | ||
| − | * [[Google Cloud Code]] and Google Kubernetes Engine (GKE) | + | ** [[Google Cloud Code]] and Google Kubernetes Engine (GKE) |
* [[Google AutoML]] | * [[Google AutoML]] | ||
| − | * [http://cloud.google.com/vision/overview/docs/ Cloud Vision] | + | ** [http://cloud.google.com/vision/overview/docs/ Cloud Vision] |
** [http://cloud.google.com/vision/ Cloud Vision API] - drag & drop picture on webpage | ** [http://cloud.google.com/vision/ Cloud Vision API] - drag & drop picture on webpage | ||
* [[Automated Machine Learning (AML) - AutoML]] | * [[Automated Machine Learning (AML) - AutoML]] | ||
| Line 44: | Line 44: | ||
* [http://ai.google/education/ Learn from ML experts at Google] | * [http://ai.google/education/ Learn from ML experts at Google] | ||
* [http://ai.google/tools/ Tools and Resources] | * [http://ai.google/tools/ Tools and Resources] | ||
| + | * [http://research.google/teams/brain/pair/ People + AI Research (PAIR)] ... devoted to advancing the research and design of people-centric AI systems. | ||
* [[Google Internet of Things (IoT)]] | * [[Google Internet of Things (IoT)]] | ||
* [[Psychlab]] for testing virtual agents in 3D environments | * [[Psychlab]] for testing virtual agents in 3D environments | ||
Revision as of 06:59, 21 August 2020
YouTube search... ...Google search
- Platforms: Machine Learning as a Service (MLaaS)
- Colaboratory (Colab) - Jupyter notebooks
- TensorFlow
- 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.
- 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.
- DeepMind - Open Source
- AI Platform
- 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
- Kubeflow Pipelines ML workflows on Kubernetes
- Google Cloud
- Google Cloud Code and Google Kubernetes Engine (GKE)
- 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
- Tools and Resources
- People + AI Research (PAIR) ... devoted to advancing the research and design of people-centric AI systems.
- Google Internet of Things (IoT)
- Psychlab for testing virtual agents in 3D environments
- Deep Reinforcement Learning (DRL) - Importance Weighted Actor-Learner Architecture (IMPALA)
- Google AIY Projects Program
- Android Neural Networks API
- BiqQuery ML (BQML)
- Firebase | Google
- DialogFlow
- Google Analytics
- Teachable Machine ...train a computer to recognize your own images, sounds, & poses
- Google Coral
- Google Sheets
The Path From Cloud AutoML to Custom Model (Cloud Next '19)
by Sara Robinson
