From 18d1ce36b5efb1ca875f152b8e53bdcffaf9bb73 Mon Sep 17 00:00:00 2001 From: Scarlett Li <39592018+scarlett2018@users.noreply.github.com> Date: Sun, 23 Aug 2020 11:16:35 +0800 Subject: [PATCH 1/2] Update BuiltinTuner.md --- docs/en_US/Tuner/BuiltinTuner.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/en_US/Tuner/BuiltinTuner.md b/docs/en_US/Tuner/BuiltinTuner.md index 48daa0a28d..3be39083ca 100644 --- a/docs/en_US/Tuner/BuiltinTuner.md +++ b/docs/en_US/Tuner/BuiltinTuner.md @@ -1,6 +1,6 @@ -# Built-in Tuners for Hyperparameter Tuning +# HyperParameter Tuning with NNI Built-in Tuners -NNI provides state-of-the-art tuning algorithms as part of our built-in tuners and makes them easy to use. Below is the brief summary of NNI's current built-in tuners: +To fit a machine/deep learning model into different tasks/problems, hyperparameters always need to be tuned. Automate the process of hyperparaeter tuning always require a good tuning algorithm. NNI has provided state-of-the-art tuning algorithms as part of our built-in tuners and makes them easy to use. Below is the brief summary of NNI's current built-in tuners: Note: Click the **Tuner's name** to get the Tuner's installation requirements, suggested scenario, and an example configuration. A link for a detailed description of each algorithm is located at the end of the suggested scenario for each tuner. Here is an [article](../CommunitySharings/HpoComparison.md) comparing different Tuners on several problems. From 94b3501d6e745d9858342b4489d52228d79de6f3 Mon Sep 17 00:00:00 2001 From: QuanluZhang Date: Thu, 3 Sep 2020 16:04:35 +0800 Subject: [PATCH 2/2] Update BuiltinTuner.md --- docs/en_US/Tuner/BuiltinTuner.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/en_US/Tuner/BuiltinTuner.md b/docs/en_US/Tuner/BuiltinTuner.md index 3be39083ca..d546d08347 100644 --- a/docs/en_US/Tuner/BuiltinTuner.md +++ b/docs/en_US/Tuner/BuiltinTuner.md @@ -1,6 +1,6 @@ # HyperParameter Tuning with NNI Built-in Tuners -To fit a machine/deep learning model into different tasks/problems, hyperparameters always need to be tuned. Automate the process of hyperparaeter tuning always require a good tuning algorithm. NNI has provided state-of-the-art tuning algorithms as part of our built-in tuners and makes them easy to use. Below is the brief summary of NNI's current built-in tuners: +To fit a machine/deep learning model into different tasks/problems, hyperparameters always need to be tuned. Automating the process of hyperparaeter tuning always requires a good tuning algorithm. NNI has provided state-of-the-art tuning algorithms as part of our built-in tuners and makes them easy to use. Below is the brief summary of NNI's current built-in tuners: Note: Click the **Tuner's name** to get the Tuner's installation requirements, suggested scenario, and an example configuration. A link for a detailed description of each algorithm is located at the end of the suggested scenario for each tuner. Here is an [article](../CommunitySharings/HpoComparison.md) comparing different Tuners on several problems.