Climatematch_Tutorial_Format.ipynb

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        "# Tutorial ?: Tutorial's name\n",
        "**Week ?, Day ?: Name of the day**\n",
        "\n",
        "**By Neuromatch Academy**\n",
        "\n",
        "__Content creators:__ Name Surname, Name Surname, Day Lead's Name\n",
        "\n",
        "__Content reviewers:__ Name Surname, Name Surname\n",
        "\n",
        "__Content editors:__ Name Surname, Name Surname\n",
        "\n",
        "__Production editors:__ Name Surname, Name Surname"
      ]
    },
    {
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      "source": [
        "**Our 2023 Sponsors**\n",
        "\n",
        "<!-- <p align='center'><img src='https://github.com/NeuromatchAcademy/widgets/blob/master/sponsors.png?raw=True'/></p> -->"
      ]
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      "source": [
        "---\n",
        "#Tutorial Objectives\n",
        "\n",
        "Use a line (---) separator from title block to objectives. You should briefly introduce your content here in a few sentences. In this tutorial, you will learn what an CMA tutorial notebook should look like. \n",
        "\n",
        "Then have a few specific objectives for this tutorial, ideally [SMART goals](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5944406/#:~:text=1%20The%20mnemonic%20SMART%E2%80%94Specific,a%20well%2Dwritten%20learning%20objective.&text=Verbs%20such%20as%20understand%2C%20know,measurable%20and%20should%20be%20avoided.). By the end of this tutorial you will be able to:\n",
        "*   Understand the format of an CMA tutorial\n",
        "*   Make code exercises, interactive demos, and group discussions the correct structure\n",
        "\n",
        "\n"
      ]
    },
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              "\n",
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              "            width=\"854\"\n",
              "            height=\"480\"\n",
              "            src=\"https://mfr.ca-1.osf.io/render?url=https://osf.io/kaq2x/?direct%26mode=render%26action=download%26mode=render\"\n",
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      "source": [
        "# @title Tutorial slides\n",
        "\n",
        "# @markdown These are the slides for the videos in all tutorials today\n",
        "from IPython.display import IFrame\n",
        "IFrame(src=f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/kaq2x/?direct%26mode=render%26action=download%26mode=render\", width=854, height=480)"
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    {
      "cell_type": "markdown",
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      "source": [
        "---\n",
        "# Setup"
      ]
    },
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      "source": [
        "In this section, we have:\n",
        "\n",
        "1. **Import cell**: imports all libraries you use in the tutorial.\n",
        "1. **Hidden Figure settings cell**: sets up the plotting style (copy exactly).\n",
        "1. **Hidden Plotting functions cell**: contains all functions used to create plots throughout the tutorial (so students don't waste time looking at boilerplate matplotlib but can here if they wish to). Please use only matplotlib for plotting for consistency.\n",
        "5. **Hidden Helper functions cell**: This should contain functions that students have previously used or that are very simple. Any helper functions that are being used for the first time and are important should be placed directly above the relevant text or exercise (see Section 1.1 for an example)."
      ]
    },
    {
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      "source": [
        "# Imports\n",
        "\n",
        "# Import only the libraries/objects that you use in this tutorial. \n",
        "\n",
        "# If any external library has to be installed, !pip install library --quiet\n",
        "# follow this order: numpy>matplotlib. \n",
        "# import widgets in hidden Figure settings cell\n",
        "\n",
        "import random\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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      "source": [
        "# @title Figure settings\n",
        "import ipywidgets as widgets       # interactive display\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "plt.style.use(\"https://raw.githubusercontent.com/NeuromatchAcademy/content-creation/main/CMA.mplstyle\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
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      "source": [
        "# @title Plotting functions\n",
        "\n",
        "# You may have functions that plot results that aren't \n",
        "# particularly interesting. You can add these here to hide them. \n",
        "\n",
        "def plotting_z(z):\n",
        "  \"\"\"This function multiplies every element in an array by a provided value\n",
        "\n",
        "  Args:\n",
        "    z (ndarray): neural activity over time, shape (T, ) where T is number of timesteps\n",
        "  \n",
        "   \"\"\"\n",
        "\n",
        "  fig, ax = plt.subplots()\n",
        "\n",
        "  ax.plot(z)\n",
        "  ax.set(\n",
        "      xlabel='Time (s)',\n",
        "      ylabel='Z',\n",
        "      title='Neural activity over time'\n",
        "      )\n"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# @title Helper functions\n",
        "\n",
        "# If any helper functions you want to hide for clarity (that has been seen before\n",
        "# or is simple/uniformative), add here\n",
        "# If helper code depends on libraries that aren't used elsewhere,\n",
        "# import those libaries here, rather than in the main import cell\n"
      ]
    },
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          "text": [
            "Random seed 2021 has been set.\n"
          ]
        }
      ],
      "source": [
        "# @title Set random seed. \n",
        "\n",
        "# @markdown Executing `set_seed(seed=seed)` you are setting the seed\n",
        "\n",
        "# for DL its critical to set the random seed so that students can have a\n",
        "# baseline to compare their results to expected results.\n",
        "# Read more here: https://pytorch.org/docs/stable/notes/randomness.html\n",
        "\n",
        "# Call `set_seed` function in the exercises to ensure reproducibility.\n",
        "import random\n",
        "import torch\n",
        "\n",
        "def set_seed(seed=None, seed_torch=True):\n",
        "  if seed is None:\n",
        "    seed = np.random.choice(2 ** 32)\n",
        "  random.seed(seed)\n",
        "  np.random.seed(seed)\n",
        "  if seed_torch:\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed_all(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "\n",
        "  print(f'Random seed {seed} has been set.')\n",
        "\n",
        "\n",
        "# In case that `DataLoader` is used\n",
        "def seed_worker(worker_id):\n",
        "  worker_seed = torch.initial_seed() % 2**32\n",
        "  np.random.seed(worker_seed)\n",
        "  random.seed(worker_seed)\n",
        "\n",
        "\n",
        "set_seed(seed=2021, seed_torch=False)  # change 2021 with any number you like"
      ]
    },
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      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
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      "source": [
        "# @title Set device (GPU or CPU). Execute `set_device()`\n",
        "# especially if torch modules used.\n",
        "\n",
        "# inform the user if the notebook uses GPU or CPU.\n",
        "\n",
        "def set_device():\n",
        "  device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "  if device != \"cuda\":\n",
        "    print(\"GPU is not enabled in this notebook. \\n\"\n",
        "          \"If you want to enable it, in the menu under `Runtime` -> \\n\"\n",
        "          \"`Hardware accelerator.` and select `GPU` from the dropdown menu\")\n",
        "  else:\n",
        "    print(\"GPU is enabled in this notebook. \\n\"\n",
        "          \"If you want to disable it, in the menu under `Runtime` -> \\n\"\n",
        "          \"`Hardware accelerator.` and select `None` from the dropdown menu\")\n",
        "\n",
        "  return device"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nULavCfq4o07"
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      "source": [
        "---\n",
        "\n",
        "# Section 1: Example of tutorial structure\n",
        "\n",
        "The above section header should be a description of what the section covers"
      ]
    },
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              "Tab(children=(Output(), Output()), _titles={'0': 'Youtube', '1': 'Bilibili'})"
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      "source": [
        "# @title Video 1: Video 1 Name\n",
        "from ipywidgets import widgets\n",
        "from IPython.display import display, IFrame, YouTubeVideo\n",
        "\n",
        "out2 = widgets.Output()\n",
        "with out2:\n",
        "  class BiliVideo(IFrame):\n",
        "    def __init__(self, id, page=1, width=400, height=300, **kwargs):\n",
        "      self.id=id\n",
        "      src = 'https://player.bilibili.com/player.html?bvid={0}&page={1}'.format(id, page)\n",
        "      super(BiliVideo, self).__init__(src, width, height, **kwargs)\n",
        "\n",
        "  video = BiliVideo(id=\"\", width=854, height=480, fs=1)\n",
        "  print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
        "  display(video)\n",
        "\n",
        "out1 = widgets.Output()\n",
        "with out1:\n",
        "  video = YouTubeVideo(id=\"\", width=854, height=480, fs=1, rel=0)\n",
        "  print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
        "  display(video)\n",
        "\n",
        "out = widgets.Tab([out1, out2])\n",
        "out.set_title(0, 'Youtube')\n",
        "out.set_title(1, 'Bilibili')\n",
        "\n",
        "display(out)"
      ]
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      "source": [
        "Text about the first concept. Don't make too long to reduce reading times during tutorial (extra text for later reference can go in appendix). Instead of long paragraphs, use bullet points of multiple shorter paragraphs. In both the videos and text, you should not \"speak in variables.\" For example, don't say \"change alpha\", say \"change the learning rate\" or \"change the learning rate, alpha\"."
      ]
    },
    {
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      "source": [
        "## Section 1.1:  Different types of activities in an CMA tutorial (subsection header)\n",
        "\n",
        "You can have students complete coding exercises, play with interactive demos, or have group discussions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZosC4SmFQnhq"
      },
      "source": [
        "### Coding Exercise 1.1: Helpful exercise name (e.g., fitting a linear regression model)\n",
        "\n",
        "This exercise fits in this subsection so is a level 3 header. We name it after the section it is in.\n",
        "\n",
        "Very briefly summarize the exercise here. The first exercise should not be too difficult, to serve as a warm-up for students. Outputs of the code cell will ideally be a plot (so students can easily check against the correct one). In limited cases, the output may be printing numbers, text, etc. In that case you need to write the solution output manually after the exercise (E.g.: \"You should see [4, 5, 6] when running the cell above\").\n",
        "\n",
        "\n",
        "Warn them when they will use a helper function. For example: In this exercise, you will also invoke `multiply_array` which multiplies an array by a scalar (a silly example).\n",
        "\n",
        "**Note**: In case of randomness and to ensure reproducibility, do not forget to use a random seed before the exercise or within the function."
      ]
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        "# @markdown *Execute this cell to enable the array multiplication function: `multiply_array`*\n",
        "\n",
        "\n",
        "def multiply_array(x, c, seed):\n",
        "  \"\"\"Multiply every element in an array by a provided value\n",
        "\n",
        "  Args:\n",
        "    x (ndarray): An array of shape (N,) \n",
        "    c (scalar): multiplicative factor\n",
        "    seed (integer): random seed\n",
        "\n",
        "  Returns:\n",
        "    ndarray: output of the multiplication \n",
        "  \"\"\"\n",
        "  np.random.seed(seed)\n",
        "  y = x * c + 4*np.random.randn()\n",
        "\n",
        "  return y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bWDbayoJwtIs"
      },
      "source": [
        "__Editor guideline:__ the above structure allows us to make the notebooks cleaner by hiding functions but still keep relevant functions close to where students will encounter them. Make sure to have the function in the markdown text so it's searchable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
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        "id": "0dYxSDmqQgAC"
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      "source": [
        "def generic_function(x, seed):\n",
        "  \"\"\"Google style doc string. Brief summary of what function does here\n",
        "  \n",
        "  Args:\n",
        "    x (ndarray): An array of shape (N,) that contains blah blah\n",
        "    seed (integer): random seed for reproducibility\n",
        "\n",
        "  Returns:\n",
        "    ndarray: The output is blah blah\n",
        "  \"\"\"\n",
        "  #################################################\n",
        "  ## TODO for students: details of what they should do ##\n",
        "  # Fill out function and remove\n",
        "  raise NotImplementedError(\"Student exercise: say what they should have done\")\n",
        "  #################################################\n",
        "  \n",
        "  # Have a comment for every line of code they need to write, and when possible have\n",
        "  # variables written with ellipses where they should fill in or ellipses where they should\n",
        "  # fill in inputs to functions\n",
        "  y = multiply_array(..., 5, seed)\n",
        "\n",
        "  # Another comment because they need to add another line of code\n",
        "  z = ...\n",
        "\n",
        "  return z\n",
        "\n",
        "\n",
        "x = np.array([4, 5, 6])\n",
        "\n",
        "# We usually define the plotting function in the hidden Helper Functions\n",
        "# so students don't have to see a bunch of boilerplate matplotlib code\n",
        "## Uncomment the code below to test your function\n",
        "# z = generic_function(x, seed)\n",
        "# plotting_z(z)"
      ]
    },
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      "source": [
        "---\n",
        "**Editor guideline for exercise solution formatting:**\n",
        "- the presence of `# to_remove solution` in the first line of solution block\n",
        "- The absence of the fenced (`#####`) block that raises a `NotImplementedError`\n",
        "- Valid code replacing all ellipses (`...`)\n",
        "- Code that uses or depends on the completed function/lines is uncommented\n",
        "- Plotting code is indented under a `with plt.xkcd():` context manager.\n",
        "- **All other comments and code should be identical**\n",
        "---"
      ]
    },
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",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 413,
              "width": 558
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# to_remove solution\n",
        "def generic_function(x, seed):\n",
        "  \"\"\"Google style doc string. Brief summary of what function does here\n",
        "  \n",
        "  Args:\n",
        "    x (ndarray): An array of shape (N,) that contains blah blah\n",
        "    seed (integer): random seed for reproducibility\n",
        "\n",
        "  Returns:\n",
        "    ndarray: The output is blah blah\n",
        "  \"\"\"\n",
        "\n",
        "  # Have a comment for every line of code they need to write, and when possible have\n",
        "  # variables written with ellipses where they should fill in or ellipses where they should\n",
        "  # fill in inputs to functions\n",
        "  y = multiply_array(x, 5, seed)\n",
        "\n",
        "  # Another comment because they need to add another line of code\n",
        "  z = y + 6\n",
        "\n",
        "  return z\n",
        "\n",
        "\n",
        "x = np.array([4, 5, 6])\n",
        "\n",
        "# We usually define the plotting function in the hidden Helper Functions\n",
        "# so students don't have to see a bunch of boilerplate matplotlib code\n",
        "## Uncomment the code below to test your function\n",
        "z = generic_function(x, seed=2021)\n",
        "with plt.xkcd():\n",
        "  plotting_z(z)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UhDupvg6W4f7"
      },
      "source": [
        "### Interactive Demo 1.1: Name of demo\n",
        "\n",
        "Here, we will demonstrate how to create a widget if you would like to use a widget to demonstrate something. Make sure the use a @title cell and hide the contents by default, because the code to make the widget is often pretty ugly and not important for the students to see. \n",
        "\n",
        "If the widget makes use of a function that must be completed as part of an exercise, you may want to re-implement the correct version of the function inside the widget cell, so that it is useful for a student who got completely stuck.\n",
        "\n",
        "There should be specific questions asked about the demo (e.g. what happens when you do this?)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "cellView": "form",
        "colab": {
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              "interactive(children=(FloatSlider(value=0.0, description='mean', max=10.0, min=-10.0, step=0.5), FloatSlider(v…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
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      ],
      "source": [
        "# @markdown Make sure you execute this cell to enable the widget!\n",
        "\n",
        "x = np.arange(-10, 11, 0.1)\n",
        "\n",
        "\n",
        "def gaussian(x, mu, sigma):\n",
        "  px = np.exp(-1 / 2 / sigma**2 * (mu - x) ** 2)\n",
        "  px = px / px.sum()\n",
        "  return px\n",
        "\n",
        "\n",
        "@widgets.interact\n",
        "def plot_gaussian(mean=(-10, 10, .5), std=(.5, 10, .5)):\n",
        "  plt.plot(x, gaussian(x, mean, std))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "O0-u0qhNgATu"
      },
      "outputs": [],
      "source": [
        "# to_remove explanation\n",
        "\n",
        "\"\"\"\n",
        "Discussion: Write a dicussion about/answers to any open-ended questions you pose (either about a demo or elsewhere)\n",
        "\n",
        "You can write a paragraph or two of nice didactic text\n",
        "within a single comment. \n",
        "\"\"\";"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4h1Y-BxcQyp"
      },
      "source": [
        "__Editor guideline:__ note that the form header for the cell above is __to_remove explanation__, not __to_remove solution__."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrTpOZZDW9qZ"
      },
      "source": [
        "maybe some more text about what exercises showed (if helpful). \n",
        "The Gaussian is: \n",
        "- bumpy in the middle\n",
        "- symmetric\n",
        "- almighty"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "-cXAncan4Xqk"
      },
      "source": [
        "### Think! 1.1: Name of discussion topic\n",
        "\n",
        "In addition to code exercises and interactive demos, you can have \"discussion exercises\" where students discuss open-ended questions. Each should roughly replace a code exercise so take about 10 minutes.\n",
        "\n",
        "E.g.: What do you think contributes to a good learning experience? Take 2 minutes to think in silence, then discuss as a group (~10 minutes).\n",
        "\n",
        "**Important**: CMA DL ONLY (not for CMA-CN): You can then include an airtable form through which students can write down their thoughts. This helps us to see how students are thinking (as we can read the responses). You should also still include the explanation cell with the answer or some discussion to aid students."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "RWWAsjjpqVTW"
      },
      "outputs": [],
      "source": [
        "# airtable\n",
        "# relevant_variable_name: text "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2jMFzfUSqX1N"
      },
      "source": [
        "__Editor guideline:__ note that to notify us where you'd like an airtable input form, use this special syntax. The first line is \"# \n",
        "\n",
        "*   First line is `# airtable`\n",
        "*   Relevant variable name on the second line should be something related to the question you're asking (an identifier). For example it could be \"learning_experience_contribs\" in this example. It should be a valid Python variable (no spaces, don't start with numbers).\n",
        "*   You can have different types of inputs: text, numerical, or multiple choice.\n",
        "    * If you want a text answer, use `# relevant_variable_name: text`\n",
        "    * If you want a numerical answer, use `# relevant_variable_name: number`\n",
        "    * If you want a multiple choice answer, use `# relevant_variable_name: [option1, option2, option3]` where you fill out what the options are. For example, `# network_arch: [MLP, CNN, RNN, Transformer]`\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "YTCPwDk7os4i"
      },
      "outputs": [],
      "source": [
        "# to_remove explanation\n",
        "\n",
        "\"\"\"\n",
        "Discussion: Write a dicussion about/answers to any open-ended questions you pose (either about a demo or elsewhere)\n",
        "\n",
        "You can write a paragraph or two of nice didactic text\n",
        "within a single comment. \n",
        "\"\"\";"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFaYDWZQ4TEF"
      },
      "source": [
        "## Section 1.2: Name"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHCBusgk74Mx"
      },
      "source": [
        "### Coding Exercise 1.2: Name\n",
        "\n",
        "This exercise is in a subsection so has a level 3 header"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EuqpiL2Av-vd"
      },
      "source": [
        "---\n",
        "# Section 2: Notation Standards\n",
        "\n",
        "For the comp neuro course, our math notation standards so far are located [here](https://docs.google.com/spreadsheets/d/11YXS39tIQb_x-wLNbbGBu1uQ9Dl4drBqUQbKpw0BQbc/edit?pli=1#gid=1974724555) (accessible from a neuromatch account). This is a work in progress so check back later. \n",
        "\n",
        "For the deep learning course, we will be using [this notation](https://www.deeplearningbook.org/contents/notation.html). You can find and use the latex [here](https://github.com/goodfeli/dlbook_notation/blob/master/notation.tex)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGr5NQmk151W"
      },
      "source": [
        "---\n",
        "# Section 3: An additional example section\n",
        "\n",
        "Separate with lines and add more content here! Depending on how the videos were created, you may or may not have a separate video for each section"
      ]
    },
    {
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              "Tab(children=(Output(), Output()), _titles={'0': 'Youtube', '1': 'Bilibili'})"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title Video 2: Video 2 Name\n",
        "from ipywidgets import widgets\n",
        "from IPython.display import display, IFrame, YouTubeVideo\n",
        "\n",
        "out2 = widgets.Output()\n",
        "with out2:\n",
        "  class BiliVideo(IFrame):\n",
        "    def __init__(self, id, page=1, width=400, height=300, **kwargs):\n",
        "      self.id=id\n",
        "      src = 'https://player.bilibili.com/player.html?bvid={0}&page={1}'.format(id, page)\n",
        "      super(BiliVideo, self).__init__(src, width, height, **kwargs)\n",
        "\n",
        "  video = BiliVideo(id=\"\", width=854, height=480, fs=1)\n",
        "  print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
        "  display(video)\n",
        "\n",
        "out1 = widgets.Output()\n",
        "with out1:\n",
        "  video = YouTubeVideo(id=\"\", width=854, height=480, fs=1, rel=0)\n",
        "  print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
        "  display(video)\n",
        "\n",
        "out = widgets.Tab([out1, out2])\n",
        "out.set_title(0, 'Youtube')\n",
        "out.set_title(1, 'Bilibili')\n",
        "\n",
        "display(out)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqA6Ajwk1cZ6"
      },
      "source": [
        "## Coding Exercise 3: Name\n",
        "\n",
        "This exercise is in this section (not in a subsection) so has a level 2 header."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Z6288_MfLEP"
      },
      "source": [
        "---\n",
        "# Section 4: DataLoader deterministically used for DL with Pytorch\n",
        "\n",
        "DataLoader will reseed workers following [Randomness in multi-process data loading algorithm](https://pytorch.org/docs/stable/data.html#data-loading-randomness). Use `worker_init_fn()` and generator to preserve reproducibility:\n",
        "\n",
        "```python\n",
        "g = torch.Generator()\n",
        "g.manual_seed(2021)\n",
        "\n",
        "DataLoader(\n",
        "    train_dataset,\n",
        "    batch_size=batch_size,\n",
        "    num_workers=num_workers,\n",
        "    worker_init_fn=seed_worker,\n",
        "    generator=g,\n",
        ")\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Z6vKsN89rN1"
      },
      "source": [
        "---\n",
        "# Summary\n",
        "\n",
        "Have a summary of what they learned with specific points.\n",
        "\n",
        "* Specific point A\n",
        "\n",
        "* Specific point B"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y6Pux46LWw7e"
      },
      "source": [
        "---\n",
        "# Bonus: Name of Bonus section\n",
        "\n",
        "Add extra text that you want students to have for reference later but that will make reading times too long during tutorials. You can have multiple Bonus sections if you'd like, just number them as Bonus 1: Name, Bonus 2: Name, etc. \n",
        "\n",
        "You should not have bonus content interleaved into the tutorial."
      ]
    }
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