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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Visualize Tensorflow's optimizers.
| Date | Stars |
|---|---|
| 2026-07-24 | 401 |
| 2026-07-25 | 401 |
| 2026-07-28 | 401 |
| 2026-07-30 | 401 |
| 2026-08-06 | 401 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# optimizer-visualization ## Visualize gradient descent optimization algorithms in Tensorflow. All methods start at the same location, specified by two variables. Both x and y variables are improved by the following Optimizers: [Adadelta documentation](https://www.tensorflow.org/api_docs/python/tf/train/AdadeltaOptimizer) [Adagrad documentation](https://www.tensorflow.org/api_docs/python/tf/train/AdagradOptimizer) [Adam documentation](https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer) [Ftrl documentation](https://www.tensorflow.org/api_docs/python/tf/train/FtrlOptimizer) [GD documentation](https://www.tensorflow.org/api_docs/python/tf/train/GradientDescentOptimizer) [Momentum documentation](https://www.tensorflow.org/api_docs/python/tf/train/MomentumOptimizer) [RMSProp documentation](https://www.tensorflow.org/api_docs/python/tf/train/RMSPropOptimizer) For an overview of each gradient descent optimization algorithms, visit [this helpful resource](http://ruder.io/optimizing-gradient-descent/). #### Numbers in figure legend indicate learning rate, specific to each Optimizer.   #### Note the optimizers' behavior when gradient is steep.     #### Note the optimizers' behavior when initial gradient is miniscule.   <!-- ## Additional Figures    #### AdadeltaOptimizer(learning_rate=50):  #### AdagradOptimizer(learning_rate=0.05):  #### AdamOptimizer(learning_rate=0.05):  #### FtrlOptimizer(learning_rate=0.05):  #### GradientDescentOptimizer(learning_rate=0.05):  #### MomentumOptimizer(learning_rate=0.05, momentum=0.9)  #### RMSPropOptimizer(learning_rate=0.05)  #### AdadeltaOptimizer(learning_rate=1000):  #### AdagradOptimizer(learning_rate=0.5):  #### AdamOptimizer(learning_rate=0.5):  #### FtrlOptimizer(learning_rate=0.5):  #### GradientDescentOptimizer(learning_rate=0.05):  #### MomentumOptimizer(learning_rate=0.05, momentum=0.9) ![](https://git
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:da7a450a5ca0e765, topic:tensorflow