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[DOCS] Updated the release notes.
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README.md

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@@ -111,7 +111,7 @@ are a few useful links:
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If you now browse to `https://127.0.0.1:6006` while training, you can see the training progress:
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<img src="assets/images/tensorboard_mnist_example_plot.png" alt="tensorboard_mnist_example_plot" width="600px">
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<img src="https://platanios.org/tensorflow_scala/assets/images/tensorboard_mnist_example_plot.png" alt="tensorboard_mnist_example_plot" width="600px">
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- Efficient interaction with the native library that avoids unnecessary copying of data. All tensors are created and
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managed by the native TensorFlow library. When they are passed to the Scala API (e.g., fetched from a TensorFlow

RELEASE.md

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@@ -33,8 +33,9 @@ various ops (e.g., `IsIntOrUInt[Int]`, `IsIntOrUInt[Long]`,
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general implementation of union types for Scala.
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Other new features include:
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- `data` module:
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- Added support for the `mapAndBatch` transformation.
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- `data` module:
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- Added support for the `mapAndBatch` transformation.
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# 0.3.0 Static Data Types and More
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Other new features include:
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- Improvements to the high-level learn API:
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- Layers can now provide and use their own parameter generator, and
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can also access the current training step
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(using `Layer.currentStep`).
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- Layers now support `.map(...)`.
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- Added support for batch normalization.
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- Added support for `tf.logSigmoid` and `tf.lrn`.
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- Added support for the following new metrics:
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- Grouped precision.
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- Precision-at-k.
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- `data` module:
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- Added support for loading the extreme classification repository
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datasets (i.e., `data.XCLoader`).
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- Added support for randomly splitting datasets.
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- Improvements to the high-level learn API:
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- Layers can now provide and use their own parameter generator, and
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can also access the current training step
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(using `Layer.currentStep`).
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- Layers now support `.map(...)`.
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- Added support for batch normalization.
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- Added support for `tf.logSigmoid` and `tf.lrn`.
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- Added support for the following new metrics:
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- Grouped precision.
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- Precision-at-k.
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- `data` module:
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- Added support for loading the extreme classification repository
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datasets (i.e., `data.XCLoader`).
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- Added support for randomly splitting datasets.
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# 0.2.4 Minor Fix
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docs/src/main/paradox/release_notes/0.3.0.md

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@@ -18,17 +18,17 @@ val t5 = t3.any() // Fails at compile-time because `any()` is only
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Other new features include:
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- Improvements to the high-level learn API:
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- Layers can now provide and use their own parameter generator, and
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can also access the current training step
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(using `Layer.currentStep`).
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- Layers now support `.map(...)`.
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- Added support for batch normalization.
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- Added support for `tf.logSigmoid` and `tf.lrn`.
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- Added support for the following new metrics:
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- Grouped precision.
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- Precision-at-k.
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- `data` module:
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- Added support for loading the extreme classification repository
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datasets (i.e., `data.XCLoader`).
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- Added support for randomly splitting datasets.
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- Improvements to the high-level learn API:
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- Layers can now provide and use their own parameter generator, and
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can also access the current training step
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(using `Layer.currentStep`).
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- Layers now support `.map(...)`.
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- Added support for batch normalization.
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- Added support for `tf.logSigmoid` and `tf.lrn`.
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- Added support for the following new metrics:
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- Grouped precision.
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- Precision-at-k.
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- `data` module:
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- Added support for loading the extreme classification repository
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datasets (i.e., `data.XCLoader`).
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- Added support for randomly splitting datasets.

docs/src/main/paradox/release_notes/0.4.0.md

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general implementation of union types for Scala.
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Other new features include:
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- `data` module:
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- Added support for the `mapAndBatch` transformation.
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- `data` module:
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- Added support for the `mapAndBatch` transformation.

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