From 0313d2234ed2759f78e30c9b4e5262f4a4e31b32 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1t=C3=A9=20Zolt=C3=A1n=20Farkas?= <mate.farkas@rwth-aachen.de> Date: Tue, 2 Aug 2022 16:38:21 +0200 Subject: [PATCH] Making some Cells Colab-compatible --- lecture1/exercise_0_python.ipynb | 10 +++++----- lecture1/exercise_0_pytorch.ipynb | 16 ++++++++-------- lecture1/exercise_0_tensorflow.ipynb | 10 +++++----- 3 files changed, 18 insertions(+), 18 deletions(-) diff --git a/lecture1/exercise_0_python.ipynb b/lecture1/exercise_0_python.ipynb index a453cea..9e55c75 100644 --- a/lecture1/exercise_0_python.ipynb +++ b/lecture1/exercise_0_python.ipynb @@ -10,7 +10,7 @@ "\n", "Welcome to this warm-up tutorial! Let's take a look at some highlights of Python!\n", "\n", - "Please execute the whole notebook now by clicking on Cell->Execute All in the menubar. In the following, cells starting with TODOs and ending with a ```?``` are tasks to be solved individually.\n", + "Please execute the whole notebook now by clicking on Execution->Execute All in the menubar. In the following, cells starting with TODOs and ending with a ```?``` are tasks to be solved individually.\n", "\n", "Let's start with the usual **hello world** program:" ] @@ -319,7 +319,7 @@ }, { "cell_type": "markdown", - "id": "6a457a29", + "id": "db6f86f8", "metadata": {}, "source": [ "### Task: <a class=\"tocSkip\"> \n", @@ -387,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "6f42ae0f", + "id": "3c16d62e", "metadata": {}, "source": [ "These functions can be also called recursively -- for this, we can take a look at the factorial function:" @@ -600,7 +600,7 @@ }, { "cell_type": "markdown", - "id": "274853db", + "id": "78db6018", "metadata": {}, "source": [ "### Task: <a class=\"tocSkip\">\n", @@ -791,7 +791,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "739732e5", + "id": "2af74b4a", "metadata": {}, "outputs": [ { diff --git a/lecture1/exercise_0_pytorch.ipynb b/lecture1/exercise_0_pytorch.ipynb index 6e53369..b3572a1 100644 --- a/lecture1/exercise_0_pytorch.ipynb +++ b/lecture1/exercise_0_pytorch.ipynb @@ -114,7 +114,7 @@ }, { "cell_type": "markdown", - "id": "42d65b3f", + "id": "0e583095", "metadata": {}, "source": [ "**Conversion** from and to ```numpy``` is also supported in an intuitive way:" @@ -123,7 +123,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "4ff55ee1", + "id": "c285066e", "metadata": {}, "outputs": [ { @@ -157,7 +157,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "b41c3f17", + "id": "bf9b99ed", "metadata": {}, "outputs": [ { @@ -198,7 +198,7 @@ }, { "cell_type": "markdown", - "id": "c7392c21", + "id": "4f283cd2", "metadata": {}, "source": [ " A key **difference in syntax** however is that ```pytorch``` knows the ```axis``` keyword as ```dim```:" @@ -207,7 +207,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "ae7c557f", + "id": "3fe72fbc", "metadata": {}, "outputs": [ { @@ -233,18 +233,18 @@ }, { "cell_type": "markdown", - "id": "cd7f9110", + "id": "f01db24d", "metadata": {}, "source": [ "### Task <a class=\"tocSkip\">\n", "Implement the mean-square difference function in pytorch:\n", - " $$ L(x, y) = \\sum_i \\frac{(x_i-y_i)^2}{N}$$" + "$ L(x, y) = \\sum_i \\frac{(x_i-y_i)^2}{N}$" ] }, { "cell_type": "code", "execution_count": 8, - "id": "8e87425d", + "id": "d674de00", "metadata": {}, "outputs": [ { diff --git a/lecture1/exercise_0_tensorflow.ipynb b/lecture1/exercise_0_tensorflow.ipynb index 33db1a3..9fefdf5 100644 --- a/lecture1/exercise_0_tensorflow.ipynb +++ b/lecture1/exercise_0_tensorflow.ipynb @@ -190,7 +190,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "57455b4b", + "id": "58c8bd1d", "metadata": {}, "outputs": [ { @@ -222,7 +222,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "6413d2a9", + "id": "dd02b475", "metadata": {}, "outputs": [ { @@ -244,18 +244,18 @@ }, { "cell_type": "markdown", - "id": "3d32f8a4", + "id": "cb3f2c1a", "metadata": {}, "source": [ "### Task: <a class=\"tocSkip\">\n", "Implement the mean-square difference function in tensorflow:\n", - " $$ L(x, y) = \\sum_i \\frac{(x_i-y_i)^2}{N}$$" + "$ L(x, y) = \\sum_i \\frac{(x_i-y_i)^2}{N}$" ] }, { "cell_type": "code", "execution_count": 8, - "id": "7aec09c9", + "id": "d925efd7", "metadata": {}, "outputs": [ { -- GitLab