From 55deaa576c3d79a35c09e7331554acb301c1a112 Mon Sep 17 00:00:00 2001 From: Thomas Simonini Date: Thu, 15 Dec 2022 16:03:34 +0100 Subject: [PATCH] Add certification info in hands-on and introduction --- notebooks/unit1/unit1.ipynb | 14 ++++++++++++++ notebooks/unit2/unit2.ipynb | 22 +++++++++++++++++----- units/en/unit0/introduction.mdx | 9 +++++++++ units/en/unit1/hands-on.mdx | 6 ++++++ units/en/unit2/hands-on.mdx | 6 ++++++ 5 files changed, 52 insertions(+), 5 deletions(-) diff --git a/notebooks/unit1/unit1.ipynb b/notebooks/unit1/unit1.ipynb index 3b58d09..aee6b51 100644 --- a/notebooks/unit1/unit1.ipynb +++ b/notebooks/unit1/unit1.ipynb @@ -166,6 +166,20 @@ "# Let's train our first Deep Reinforcement Learning agent and upload it to the Hub 🚀\n" ] }, + { + "cell_type": "markdown", + "source": [ + "## Get a certificate\n", + "To validate this hands-on for the [certification process](https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process), you need to push your trained model to the Hub and **get a result of >= 200**.\n", + "\n", + "To find your result, go to the [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) and find your model, **the result = mean_reward - std of reward**\n", + "\n", + "For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process" + ], + "metadata": { + "id": "qDploC3jSH99" + } + }, { "cell_type": "markdown", "source": [ diff --git a/notebooks/unit2/unit2.ipynb b/notebooks/unit2/unit2.ipynb index 5555554..f1ff2cd 100644 --- a/notebooks/unit2/unit2.ipynb +++ b/notebooks/unit2/unit2.ipynb @@ -3,8 +3,7 @@ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "id": "view-in-github" }, "source": [ "\"Open" @@ -169,6 +168,20 @@ "id": "HEtx8Y8MqKfH" } }, + { + "cell_type": "markdown", + "source": [ + "\n", + "To validate this hands-on for the [certification process](https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process), you need to push your trained Taxi model to the Hub and **get a result of >= 4.5**.\n", + "\n", + "To find your result, go to the [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) and find your model, **the result = mean_reward - std of reward**\n", + "\n", + "For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process" + ], + "metadata": { + "id": "Kdxb1IhzTn0v" + } + }, { "cell_type": "markdown", "source": [ @@ -1734,8 +1747,7 @@ "Ji_UrI5l2zzn", "67OdoKL63eDD", "B2_-8b8z5k54" - ], - "include_colab_link": true + ] }, "gpuClass": "standard", "kernelspec": { @@ -1748,4 +1760,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/units/en/unit0/introduction.mdx b/units/en/unit0/introduction.mdx index 4ab31b0..3118d0d 100644 --- a/units/en/unit0/introduction.mdx +++ b/units/en/unit0/introduction.mdx @@ -53,12 +53,21 @@ The course is composed of: You can choose to follow this course either: - *To get a certificate of completion*: you need to complete 80% of the assignments before the end of March 2023. +- *To get a certificate of honors*: you need to complete 100% of the assignments before the end of March 2023. - *As a simple audit*: you can participate in all challenges and do assignments if you want, but you have no deadlines. Both paths **are completely free**. Whatever path you choose, we advise you **to follow the recommended pace to enjoy the course and challenges with your fellow classmates.** You don't need to tell us which path you choose. At the end of March, when we verify the assignments **if you get more than 80% of the assignments done, you'll get a certificate.** +## The Certification Process [[certification-process]] + +The certification process is **completely free**: + +- *To get a certificate of completion*: you need to complete 80% of the assignments before the end of March 2023. +- *To get a certificate of honors*: you need to complete 100% of the assignments before the end of March 2023. + +Course certification ## How to get most of the course? [[advice]] diff --git a/units/en/unit1/hands-on.mdx b/units/en/unit1/hands-on.mdx index 2c65154..419aefd 100644 --- a/units/en/unit1/hands-on.mdx +++ b/units/en/unit1/hands-on.mdx @@ -18,6 +18,12 @@ And finally, you'll **upload this trained agent to the Hugging Face Hub 🤗, a Thanks to our leaderboard, you'll be able to compare your results with other classmates and exchange the best practices to improve your agent's scores. Who will win the challenge for Unit 1 🏆? +To validate this hands-on for the [certification process](https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process), you need to push your trained model to the Hub and **get a result of >= 200**. + +To find your result, go to the [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) and find your model, **the result = mean_reward - std of reward** + +For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process + So let's get started! 🚀 **To start the hands-on click on Open In Colab button** 👇 : diff --git a/units/en/unit2/hands-on.mdx b/units/en/unit2/hands-on.mdx index 08c63d7..71c0151 100644 --- a/units/en/unit2/hands-on.mdx +++ b/units/en/unit2/hands-on.mdx @@ -16,6 +16,12 @@ Now that we studied the Q-Learning algorithm, let's implement it from scratch an Thanks to a [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard), you'll be able to compare your results with other classmates and exchange the best practices to improve your agent's scores. Who will win the challenge for Unit 2? +To validate this hands-on for the [certification process](https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process), you need to push your trained Taxi model to the Hub and **get a result of >= 4.5**. + +To find your result, go to the [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) and find your model, **the result = mean_reward - std of reward** + +For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process + **To start the hands-on click on Open In Colab button** 👇 :