From d3acac93215e28d9ad7c858b464737361cbbdcf0 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: Fri, 29 Jul 2022 14:53:23 +0200
Subject: [PATCH] python tutorial material ready
---
lecture1/exercise_0_python.ipynb | 379 ++++++++++++++++++++++++---
lecture1/exercise_0_tensorflow.ipynb | 42 +++
2 files changed, 378 insertions(+), 43 deletions(-)
create mode 100644 lecture1/exercise_0_tensorflow.ipynb
diff --git a/lecture1/exercise_0_python.ipynb b/lecture1/exercise_0_python.ipynb
index 46ece82..c9af740 100644
--- a/lecture1/exercise_0_python.ipynb
+++ b/lecture1/exercise_0_python.ipynb
@@ -17,7 +17,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 1,
"id": "c8562e26",
"metadata": {
"scrolled": false
@@ -45,7 +45,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 2,
"id": "bea4da9f",
"metadata": {},
"outputs": [
@@ -72,7 +72,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 3,
"id": "6b5d414b",
"metadata": {},
"outputs": [],
@@ -82,7 +82,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 4,
"id": "d69d90e9",
"metadata": {},
"outputs": [
@@ -115,7 +115,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 5,
"id": "f7179786",
"metadata": {},
"outputs": [],
@@ -130,7 +130,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 6,
"id": "d0586d1b",
"metadata": {},
"outputs": [
@@ -139,7 +139,7 @@
"evalue": "invalid syntax (998780084.py, line 3)",
"output_type": "error",
"traceback": [
- "\u001b[0;36m File \u001b[0;32m\"/tmp/ipykernel_51154/998780084.py\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m print(\"The second element in a is:\", None,\"(should be 3)\")?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+ "\u001b[0;36m Input \u001b[0;32mIn [6]\u001b[0;36m\u001b[0m\n\u001b[0;31m print(\"The second element in a is:\", None,\"(should be 3)\")?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
@@ -160,7 +160,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 12,
"id": "86b8e68c",
"metadata": {},
"outputs": [
@@ -169,7 +169,7 @@
"evalue": "invalid syntax (2320046825.py, line 2)",
"output_type": "error",
"traceback": [
- "\u001b[0;36m File \u001b[0;32m\"/tmp/ipykernel_51154/2320046825.py\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m print(\"13 x 14.5 =\", \"?\")?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+ "\u001b[0;36m Input \u001b[0;32mIn [12]\u001b[0;36m\u001b[0m\n\u001b[0;31m print(\"13 x 14.5 =\", \"?\")?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
@@ -178,9 +178,35 @@
"print(\"13 x 14.5 =\", \"?\")?"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "32b1985e",
+ "metadata": {},
+ "source": [
+ "You can use the **double star** \\*\\*-operator to raise number to the **n-th power**:"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 13,
+ "id": "8c45add3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 4 9 16 1024\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(1**2, 2**2, 3**2, 4**2, 32**2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
"id": "29eac6ed",
"metadata": {},
"outputs": [
@@ -189,7 +215,7 @@
"evalue": "invalid syntax (2665876267.py, line 2)",
"output_type": "error",
"traceback": [
- "\u001b[0;36m File \u001b[0;32m\"/tmp/ipykernel_51154/2665876267.py\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m print(\"13 + True =\", \"?\")?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+ "\u001b[0;36m Input \u001b[0;32mIn [14]\u001b[0;36m\u001b[0m\n\u001b[0;31m print(\"13 + True =\", \"?\")?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
@@ -209,7 +235,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 15,
"id": "b0a7cbe1",
"metadata": {},
"outputs": [
@@ -218,7 +244,7 @@
"evalue": "invalid syntax (816995051.py, line 2)",
"output_type": "error",
"traceback": [
- "\u001b[0;36m File \u001b[0;32m\"/tmp/ipykernel_51154/816995051.py\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m print((a==14) and not (c<d))?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+ "\u001b[0;36m Input \u001b[0;32mIn [15]\u001b[0;36m\u001b[0m\n\u001b[0;31m print((a==14) and not (c<d))?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
@@ -233,13 +259,13 @@
"metadata": {},
"source": [
"## Control Flow Tools\n",
- "For pre-test loops you can use ```while```. Beware that in Python indented blocks of code belongs to the same parent line (similarly to ```{...}``` in C); mixing indentations yields an ```IndentationError```."
+ "For pre-test loops you can use ```while```. Beware that in Python indented blocks of code belong to the same parent line (similarly to ```{...}``` in C); mixing indentations raises an ```IndentationError```."
]
},
{
"cell_type": "code",
- "execution_count": 34,
- "id": "bc0a1aa7",
+ "execution_count": 16,
+ "id": "5f42b63f",
"metadata": {},
"outputs": [
{
@@ -247,7 +273,7 @@
"evalue": "unexpected indent (3109148489.py, line 5)",
"output_type": "error",
"traceback": [
- "\u001b[0;36m File \u001b[0;32m\"/tmp/ipykernel_51154/3109148489.py\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m i = i + 1\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
+ "\u001b[0;36m Input \u001b[0;32mIn [16]\u001b[0;36m\u001b[0m\n\u001b[0;31m i = i + 1\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
]
}
],
@@ -261,7 +287,7 @@
},
{
"cell_type": "markdown",
- "id": "f5d33ba2",
+ "id": "118d8935",
"metadata": {},
"source": [
"Using ```for``` and the iterable ```range(n)``` a for-like loop can be created:"
@@ -269,8 +295,8 @@
},
{
"cell_type": "code",
- "execution_count": 45,
- "id": "d26a4b8a",
+ "execution_count": 17,
+ "id": "7740d793",
"metadata": {},
"outputs": [
{
@@ -278,7 +304,7 @@
"evalue": "invalid syntax (756008327.py, line 5)",
"output_type": "error",
"traceback": [
- "\u001b[0;36m File \u001b[0;32m\"/tmp/ipykernel_51154/756008327.py\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m print(sum)?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+ "\u001b[0;36m Input \u001b[0;32mIn [17]\u001b[0;36m\u001b[0m\n\u001b[0;31m print(sum)?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
@@ -292,7 +318,7 @@
},
{
"cell_type": "markdown",
- "id": "b3730030",
+ "id": "fd2059e5",
"metadata": {},
"source": [
"For **conditional branching**, the usual keywords ```if``` and ```else``` are defined. Loops can be stopped with ```break``` and skipped with ```continue```."
@@ -300,8 +326,8 @@
},
{
"cell_type": "code",
- "execution_count": 66,
- "id": "9a89a44c",
+ "execution_count": 18,
+ "id": "0c47c5d6",
"metadata": {},
"outputs": [
{
@@ -309,7 +335,7 @@
"evalue": "invalid syntax (2853117672.py, line 9)",
"output_type": "error",
"traceback": [
- "\u001b[0;36m File \u001b[0;32m\"/tmp/ipykernel_51154/2853117672.py\"\u001b[0;36m, line \u001b[0;32m9\u001b[0m\n\u001b[0;31m print(\"else block\")?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+ "\u001b[0;36m Input \u001b[0;32mIn [18]\u001b[0;36m\u001b[0m\n\u001b[0;31m print(\"else block\")?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
@@ -327,7 +353,7 @@
},
{
"cell_type": "markdown",
- "id": "2b795be3",
+ "id": "f51fc582",
"metadata": {},
"source": [
"## Functions\n",
@@ -336,28 +362,32 @@
},
{
"cell_type": "code",
- "execution_count": 69,
- "id": "1768ca2b",
+ "execution_count": 32,
+ "id": "d5f33c84",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Hello John Cleese!\n"
+ "Hello John Cleese!\n",
+ "Hello Graham Chapman!\n",
+ "Hello Eric Idle!\n"
]
}
],
"source": [
"def get_greeting(name):\n",
" return \"Hello \" + name +\"!\"\n",
- "print(get_greeting(\"John Cleese\"))"
+ "print(get_greeting(\"John Cleese\"))\n",
+ "print(get_greeting(\"Graham Chapman\"))\n",
+ "print(get_greeting(\"Eric Idle\"))"
]
},
{
"cell_type": "code",
- "execution_count": 82,
- "id": "e123d01c",
+ "execution_count": 20,
+ "id": "04034c0e",
"metadata": {},
"outputs": [
{
@@ -365,7 +395,7 @@
"evalue": "invalid syntax (1882212969.py, line 5)",
"output_type": "error",
"traceback": [
- "\u001b[0;36m File \u001b[0;32m\"/tmp/ipykernel_51154/1882212969.py\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m factorial(10)?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+ "\u001b[0;36m Input \u001b[0;32mIn [20]\u001b[0;36m\u001b[0m\n\u001b[0;31m factorial(10)?\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
@@ -379,17 +409,17 @@
},
{
"cell_type": "markdown",
- "id": "179e54d9",
+ "id": "49898002",
"metadata": {},
"source": [
"## Classes\n",
- "Classes are high-level objects for storing and managing primitives. They are initialized with ```class class_name:``` after which the constructor ```__init__(self, args)``` is automatically called. Data in classes are stored using the ```self``` keyword, which are publicly accessible by default."
+ "Classes are high-level objects for storing and managing primitives. They are initialized with ```class class_name:``` after which the constructor ```__init__(self, args)``` is automatically called. Data in classes are stored using the ```self``` keyword and are publicly accessible by default."
]
},
{
"cell_type": "code",
- "execution_count": 79,
- "id": "d399a53d",
+ "execution_count": 21,
+ "id": "dd4ae61f",
"metadata": {},
"outputs": [
{
@@ -417,17 +447,17 @@
},
{
"cell_type": "markdown",
- "id": "cd09aaa1",
+ "id": "0d914e99",
"metadata": {},
"source": [
"## Class Inheritance\n",
- "A class can inherit the properties of the parent class. These properties can be freely overriden:"
+ "A class can inherit the properties of its parent class. These properties can be freely overriden:"
]
},
{
"cell_type": "code",
- "execution_count": 81,
- "id": "60a46ff6",
+ "execution_count": 22,
+ "id": "caf893b1",
"metadata": {},
"outputs": [
{
@@ -442,12 +472,275 @@
"class Student(Person):\n",
" def print_info(self):\n",
" \"\"\"\n",
- " Beware that name is inherited from 'Person'\n",
+ " Beware that the attribute name has been inherited from 'Person'\n",
" \"\"\"\n",
" print(self.name, \"is a student!\")\n",
"bob = Student(\"Bob\", 20)\n",
"bob.print_info()"
]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "39f231b0",
+ "metadata": {},
+ "source": [
+ "## Numpy Basics\n",
+ "Numpy is an performant, easy-to-use scientific library for numeric calculations. You will often encouter cases where numpy can perform calculations several orders of magnitues faster due its C-backend and ability to process data in a vectorized (not individuallistic) form."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "dfaeae8a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np # numpy is usually imported this way"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "935518b0",
+ "metadata": {},
+ "source": [
+ "### Numpy One Dimensional Array Operations\n",
+ "Numpy operates on arrays element-wise meaning elementary operations are performed in an intuitive vectorized form."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "2aff9b94",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[3. 3. 3. 3. 3. 3. 3. 3. 3. 3.]\n",
+ "[11 22 33 44 55 66 77] <class 'numpy.ndarray'>\n"
+ ]
+ }
+ ],
+ "source": [
+ "zero_array = np.zeros(10) # creates an an array of 10 zeros\n",
+ "one_array = np.ones(10) # creates an array of... guess what!\n",
+ "two_array = np.ones(10)*2 # this one is a bit more tricky\n",
+ "\n",
+ "three_array = one_array + two_array # arrays are added element-wise\n",
+ "print(three_array)\n",
+ "\n",
+ "# You can also create numpy arrays from python lists:\n",
+ "np_array_from_python_list = np.array([11, 22, 33, 44, 55, 66, 77])\n",
+ "print(np_array_from_python_list, type(np_array_from_python_list))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e6057042",
+ "metadata": {},
+ "source": [
+ "For this to work however the arrays must be of the same shape, otherwise a ```ValueError``` will be raised. In order to avoid this error, you can always access the shape of an array using the ```array.shape``` syntax:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "1141c56f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape of array1 (10,)\n",
+ "Shape of array2 (5,)\n"
+ ]
+ },
+ {
+ "ename": "NameError",
+ "evalue": "name 'np_array1' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [25]\u001b[0m, in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m array2 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m10\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m5\u001b[39m) \u001b[38;5;66;03m# [10, 10, 10, 10, 10 ]\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShape of array2\u001b[39m\u001b[38;5;124m\"\u001b[39m, array2\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;66;03m# shape (5,)\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mnp_array1\u001b[49m\u001b[38;5;241m/\u001b[39mnp_array2)\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'np_array1' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "array1 = np.arange(10) # [0, 1, 2, ..., 9]\n",
+ "print(\"Shape of array1\", array1.shape) # shape: (10,)\n",
+ "array2 = np.array([10]*5) # [10, 10, 10, 10, 10 ]\n",
+ "print(\"Shape of array2\", array2.shape) # shape (5,)\n",
+ "print(np_array1/np_array2) # Voilà ValueError due to conflicting shapes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d236a438",
+ "metadata": {},
+ "source": [
+ "### Numpy Arrays of Multiple Dimensions\n",
+ "Numpy supports the creation of n-dimensional arrays (arrays, matrices, tensors). Let's say you wish to generate 5 normally distributed samples of size 1000 with means 0, 10, 20, 30 and 40 and standard deviation 1. For this, you can first generate 5\\*1000 samples around 0, put them into a marix of shape (5, 1000) and shift each distribution to their respective means."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "47d6ecce",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape of the original dataset: (5000,)\n",
+ "Shape of the reshaped dataset: (5, 1000)\n"
+ ]
+ }
+ ],
+ "source": [
+ "ndists = 5\n",
+ "sample_size = 1000\n",
+ "dataset = np.random.normal(0, 1, ndists*sample_size) # Dataset generation\n",
+ "print(\"Shape of the original dataset:\", dataset.shape) # Printing the dataset shape\n",
+ "dataset_reshaped = dataset.reshape(ndists, sample_size) # Reshaping it to (5, 1000)\n",
+ "print(\"Shape of the reshaped dataset:\", dataset_reshaped.shape) # Printing the new shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bffbc39",
+ "metadata": {},
+ "source": [
+ "Now we can define our shift vector, extend it with a new \"dimension\" (called axis, i.e. bring it into the shape (5, 1)), such that all elements at the second index \"see\" a scalar."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "f2ea360c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shift vector shape: (5, 1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "shift_vector = np.arange(5)*10 # np.arange(n) creates an array of integers from 0 to n-1\n",
+ "shift_vector = shift_vector[:, np.newaxis] # np.newaxis takes care of the new axis creation\n",
+ "print(\"Shift vector shape:\", shift_vector.shape) # observe that reshape has not been called"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "64fdffa1",
+ "metadata": {},
+ "source": [
+ "Now our gaussians can be shifted to their right places!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "f0177d95",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dataset_shifted = shift_vector + dataset_reshaped # shape (5, 1) + shape (5, 1000) = shape (5, 1000)\n",
+ "\n",
+ "import matplotlib.pyplot as plt # import the plotting library to showcase the results\n",
+ "for hist in dataset_shifted: # Iterate over every distribution\n",
+ " plt.hist(hist)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "439f4bf4",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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zMZPSBzcDv5rkMLNTsrcm+SuW4ec33Ofz0Qlv2gts6Za3AE+MsZaRSxLgUWCmqh7q2TQx/ZBkRZIf6ZZ/CPhF4AUmpA+q6v6qWl1VU8z+23+qqn6DZfj5vUO1jyTvYXbe7cyjE3aMt6LRS/IZ4BZmH216HPgw8DfAHmAt8DJwZ1XNPenajCQ/D/wTcIA351sfYHbefSL6IcnPMnvC8DJmB397quqPkvwoE9IHZyS5Bfi9qnrvcvz8hrskNchpGUlqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGvR/DvztBNbwyk4AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# ... or you check the documentation and solve the problem directly by invoking:\n",
+ "dataset_shifted = np.random.normal(loc=np.arange(5)*10, scale=np.ones(5), size=(1000, 5)).T\n",
+ "for hist in dataset_shifted:\n",
+ " plt.hist(hist)\n",
+ "\n",
+ "# Always check the docs before inventing something \"new\"!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7a826bff",
+ "metadata": {},
+ "source": [
+ "### Numpy Miscellaneous:\n",
+ "Below you can find a non-exhaustive list of built-in numpy functions for quick data analysis:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "973bd72b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Basic array operations:\n",
+ "Sum 5050\n",
+ "Mean [ 0.08110435 9.98545632 20.02933072 30.02389687 39.97474169]\n",
+ "Std [0.99851965 0.97608616 1.0021044 0.98741231 1.00744611]\n",
+ "Flattened array: [-0.44605952 -0.22217352 -0.25286257 ... 41.29848664 39.92391046\n",
+ " 39.72392673]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Basic array operations:\")\n",
+ "print(\"Sum\", np.sum(np.arange(101))) # Sum of all numbers from 0 to 100\n",
+ "print(\"Mean\", np.mean(dataset_shifted, axis=1)) # Mean of the generated gaussians from above...\n",
+ "print(\"Std\", dataset_shifted.std(axis=1)) # ...and their respective standard deviations with a different syntax\n",
+ "\n",
+ "print(\"Flattened array:\", dataset_shifted.flatten()) # .flatten() makes an array 1D"
+ ]
}
],
"metadata": {
@@ -466,7 +759,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.7"
+ "version": "3.9.12"
}
},
"nbformat": 4,
diff --git a/lecture1/exercise_0_tensorflow.ipynb b/lecture1/exercise_0_tensorflow.ipynb
new file mode 100644
index 0000000..51649a1
--- /dev/null
+++ b/lecture1/exercise_0_tensorflow.ipynb
@@ -0,0 +1,42 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "269d507c",
+ "metadata": {},
+ "source": [
+ "# Tensorflow Tutorial\n",
+ "---------------------------------"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fb7e3964",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--
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