Difference between revisions of "TensorBoard"
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[http://www.youtube.com/results?search_query=tensorboard YouTube search...] | [http://www.youtube.com/results?search_query=tensorboard YouTube search...] | ||
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* [http://www.tensorflow.org/guide/graph_viz TensorBoard: Graph Visualization] | * [http://www.tensorflow.org/guide/graph_viz TensorBoard: Graph Visualization] | ||
* [http://playground.tensorflow.org Neural Networks Playground] | * [http://playground.tensorflow.org Neural Networks Playground] | ||
| − | * [[Visualization]] | + | * [[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]] |
* [[Colaboratory]] (Colab) | * [[Colaboratory]] (Colab) | ||
* [[Data Flow Graph (DFG)]] | * [[Data Flow Graph (DFG)]] | ||
Revision as of 06:42, 14 July 2023
YouTube search... ...Google search
- TensorBoard | Google ...TensorFlow's visualization toolkit
- TensorFlow
- TensorFlow Playground
- TensorBoard: Graph Visualization
- Neural Networks Playground
- Analytics ... Visualization ... Graphical Tools ... Diagrams & Business Analysis ... Requirements ... Loop ... Bayes ... Network Pattern
- Colaboratory (Colab)
- Data Flow Graph (DFG)
- TensorWatch
- Principal Component Analysis (PCA) ...linear
- Embedding
- T-Distributed Stochastic Neighbor Embedding (t-SNE) ...non-linear
- Graphical Tools for Modeling AI Components
- Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow | K. Wongsuphasawat, D. Smilkov, J. Wexler, J. Wilson, D. Mane, D. Fritz, D. Krishnan, F. Viegas, and M. Wattenberg
In machine learning, to improve something you often need to be able to measure it. TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more. provides the visualization and tooling needed for machine learning experimentation:
- Tracking and visualizing metrics such as loss and accuracy
- Visualizing the model graph (ops and layers)
- Viewing histograms of weights, biases, or other tensors as they change over time
- Projecting embeddings to a lower dimensional space
- Displaying images, text, and audio data
- Profiling TensorFlow programs
