markdown stringlengths 0 37k | code stringlengths 1 33.3k | path stringlengths 8 215 | repo_name stringlengths 6 77 | license stringclasses 15
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Train a model for the competition
The code cell above trains a Random Forest model on train_X and train_y.
Use the code cell below to build a Random Forest model and train it on all of X and y. | # To improve accuracy, create a new Random Forest model which you will train on all training data
rf_model_on_full_data = ____
# fit rf_model_on_full_data on all data from the training data
____ | notebooks/machine_learning/raw/ex7.ipynb | Kaggle/learntools | apache-2.0 |
Now, read the file of "test" data, and apply your model to make predictions. | # path to file you will use for predictions
test_data_path = '../input/test.csv'
# read test data file using pandas
test_data = ____
# create test_X which comes from test_data but includes only the columns you used for prediction.
# The list of columns is stored in a variable called features
test_X = ____
# make pre... | notebooks/machine_learning/raw/ex7.ipynb | Kaggle/learntools | apache-2.0 |
Before submitting, run a check to make sure your test_preds have the right format. | # Check your answer (To get credit for completing the exercise, you must get a "Correct" result!)
step_1.check()
# step_1.solution()
#%%RM_IF(PROD)%%
rf_model_on_full_data = RandomForestRegressor()
rf_model_on_full_data.fit(X, y)
test_data_path = '../input/test.csv'
test_data = pd.read_csv(test_data_path)
test_X = tes... | notebooks/machine_learning/raw/ex7.ipynb | Kaggle/learntools | apache-2.0 |
Generate a submission
Run the code cell below to generate a CSV file with your predictions that you can use to submit to the competition. | # Run the code to save predictions in the format used for competition scoring
output = pd.DataFrame({'Id': test_data.Id,
'SalePrice': test_preds})
output.to_csv('submission.csv', index=False) | notebooks/machine_learning/raw/ex7.ipynb | Kaggle/learntools | apache-2.0 |
Examples of plugins usage in folium
In this notebook we show a few illustrations of folium's plugin extensions.
This is a development notebook
Adds a button to enable/disable zoom scrolling.
ScrollZoomToggler | from folium import plugins
m = folium.Map([45, 3], zoom_start=4)
plugins.ScrollZoomToggler().add_to(m)
m.save(os.path.join('results', 'Plugins_0.html'))
m | examples/Plugins.ipynb | shankari/folium | mit |
Fullscreen | m = folium.Map(location=[41.9, -97.3], zoom_start=4)
plugins.Fullscreen(
position='topright',
title='Expand me',
titleCancel='Exit me',
forceSeparateButton=True).add_to(m)
m.save(os.path.join('results', 'Plugins_4.html'))
m # Click on the top right button. | examples/Plugins.ipynb | shankari/folium | mit |
Shape error | def some_method(data):
a = data[:,0:2]
c = data[:,1]
s = (a + c)
return tf.sqrt(tf.matmul(s, tf.transpose(s)))
with tf.Session() as sess:
fake_data = tf.constant([
[5.0, 3.0, 7.1],
[2.3, 4.1, 4.8],
[2.8, 4.2, 5.6],
[2.9, 8.3, 7.3]
])
print(sess.run(some_method(fake_data)))
def ... | courses/machine_learning/deepdive/03_tensorflow/debug_demo.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Vector vs scalar | def some_method(data):
print(data.get_shape())
a = data[:,0:2]
print(a.get_shape())
c = data[:,1:3]
print(c.get_shape())
s = (a + c)
return tf.sqrt(tf.matmul(s, tf.transpose(s)))
with tf.Session() as sess:
fake_data = tf.constant([5.0, 3.0, 7.1])
print(sess.run(some_method(fake_data)))
def some_meth... | courses/machine_learning/deepdive/03_tensorflow/debug_demo.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Type error | def some_method(a, b):
s = (a + b)
return tf.sqrt(tf.matmul(s, tf.transpose(s)))
with tf.Session() as sess:
fake_a = tf.constant([
[5.0, 3.0, 7.1],
[2.3, 4.1, 4.8],
])
fake_b = tf.constant([
[2, 4, 5],
[2, 8, 7]
])
print(sess.run(some_method(fake_a, fake_b)))
def some_method(... | courses/machine_learning/deepdive/03_tensorflow/debug_demo.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
TensorFlow debugger
Wrap your normal Session object with tf_debug.LocalCLIDebugWrapperSession | import tensorflow as tf
from tensorflow.python import debug as tf_debug
def some_method(a, b):
b = tf.cast(b, tf.float32)
s = (a / b)
s2 = tf.matmul(s, tf.transpose(s))
return tf.sqrt(s2)
with tf.Session() as sess:
fake_a = [
[5.0, 3.0, 7.1],
[2.3, 4.1, 4.8],
]
fake_b = [
[2, 0, 5],
... | courses/machine_learning/deepdive/03_tensorflow/debug_demo.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
In the tfdbg> window that comes up, try the following:
* run -f has_inf_or_nan
* Notice that several tensors are dumped once the filter criterion is met
* List the inputs to a specific tensor:
* li transpose:0
* Print the value of a tensor
* pt transpose:0
* Where is the inf?
Visit https://www.tensorflow.org/programme... | %%writefile debugger.py
import tensorflow as tf
def some_method(a, b):
b = tf.cast(b, tf.float32)
s = (a / b)
print_ab = tf.Print(s, [a, b])
s = tf.where(tf.is_nan(s), print_ab, s)
return tf.sqrt(tf.matmul(s, tf.transpose(s)))
with tf.Session() as sess:
fake_a = tf.constant([
[5.0, 3.0, 7.1],
... | courses/machine_learning/deepdive/03_tensorflow/debug_demo.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Execute the python script | %%bash
python debugger.py | courses/machine_learning/deepdive/03_tensorflow/debug_demo.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Alpha-Beta Pruning with Progressive Deepening, Move Ordering, and Memoization
The function pd_evaluate takes three arguments:
- State is the current state of the game,
- limit determines how deep the game tree is searched,
- f is either the function maxValue or the function minValue.
The function pd_evaluate uses p... | import time
def pd_evaluate(State, time_limit, f):
start = time.time()
limit = 0
while True:
value = evaluate(State, limit, f)
stop = time.time()
if value in [-1, 1] or stop - start > time_limit:
print(f'searched to depth {limit}, using {round(stop - start, 3)} seconds')... | Python/3 Games/Game.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
The function evaluate takes five arguments:
- State is the current state of the game,
- limit determines the lookahead. To be more precise, it is the number of half-moves that are investigated to compute the value. If limit is 0 and the game has not ended, the game is evaluated via the function heuristic. This functi... | def evaluate(State, limit, f, alpha=-1, beta=1):
global gCache
if (State, limit) in gCache:
flag, v = gCache[(State, limit)]
if flag == '=':
return v
if flag == '≤':
if v <= alpha:
return v
elif alpha < v < beta:
w =... | Python/3 Games/Game.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
The function maxValue satisfies the following specification:
- $\alpha \leq \texttt{value}(s) \leq \beta \;\rightarrow\;\texttt{maxValue}(s, l, \alpha, \beta) = \texttt{value}(s)$
- $\texttt{value}(s) \leq \alpha \;\rightarrow\; \texttt{maxValue}(s, l, \alpha, \beta) \leq \alpha$
- $\beta \leq \texttt{value}(s) \;\righ... | def maxValue(State, limit, alpha=-1, beta=1):
if finished(State):
return utility(State)
if limit == 0:
return heuristic(State)
value = alpha
NextStates = next_states(State, gPlayers[0])
if len(NextStates) == 1: # singular value extension
return evaluate(NextStates[0], l... | Python/3 Games/Game.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
The function minValue satisfies the following specification:
- $\alpha \leq \texttt{value}(s) \leq \beta \;\rightarrow\;\texttt{minValue}(s, l, \alpha, \beta) = \texttt{value}(s)$
- $\texttt{value}(s) \leq \alpha \;\rightarrow\; \texttt{minValue}(s, l, \alpha, \beta) \leq \alpha$
- $\beta \leq \texttt{value}(s) \;\righ... | def minValue(State, limit, alpha=-1, beta=1):
if finished(State):
return utility(State)
if limit == 0:
return heuristic(State)
value = beta
NextStates = next_states(State, gPlayers[1])
if len(NextStates) == 1:
return evaluate(NextStates[0], limit, maxValue, alpha, value)... | Python/3 Games/Game.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
In the state shown below, Red can force a win by pushing his stones in the 6th row. Due to this fact, *alpha-beta pruning is able to prune large parts of the search path and hence the evaluation is fast. | canvas = create_canvas()
draw(gTestState, canvas, '?')
gCache = {}
%%time
value, limit = pd_evaluate(gTestState, 10, maxValue)
value
len(gCache)
gCache = {}
%%time
value, limit = pd_evaluate(gStart, 5, maxValue)
value
len(gCache) | Python/3 Games/Game.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
In order to evaluate the effect of progressive deepening, we reset the cache and can then evaluate the test state without progressive deepening. | gCache = {}
%%time
value = evaluate(gTestState, 8, maxValue)
value
len(gCache) | Python/3 Games/Game.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
Playing the Game
The function best_move takes two arguments:
- State is the current state of the game,
- limit is the depth limit of the recursion.
The function best_move returns a pair of the form $(v, s)$ where $s$ is a state and $v$ is the value of this state. The state $s$ is a state that is reached from State if ... | def best_move(State, time_limit):
NextStates = next_states(State, gPlayers[0])
if len(NextStates) == 1:
return pd_evaluate(State, time_limit, maxValue), NextStates[0]
bestValue, limit = pd_evaluate(State, time_limit, maxValue)
BestMoves = [s for s in NextStates
if evaluate... | Python/3 Games/Game.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
The function play_game plays on the given canvas. The game played is specified indirectly by specifying the following:
- Start is a global variable defining the start state of the game.
- next_states is a function such that $\texttt{next_states}(s, p)$ computes the set of all possible states that can be reached from s... | def play_game(canvas, time_limit):
global gCache, gMoveCounter
State = gStart
while (True):
gCache = {}
firstPlayer = gPlayers[0]
val, State = best_move(State, time_limit)
draw(State, canvas, f'value = {round(val, 2)}.')
if finished(State):
final_msg(St... | Python/3 Games/Game.ipynb | karlstroetmann/Artificial-Intelligence | gpl-2.0 |
What are the metrics for "holding the position"? | print('Sharpe ratio: {}\nCum. Ret.: {}\nAVG_DRET: {}\nSTD_DRET: {}\nFinal value: {}'.format(*value_eval(pd.DataFrame(data_test_df['Close'].iloc[TEST_DAYS_AHEAD:]))))
import pickle
with open('../../data/dyna_10000_states_full_training.pkl', 'wb') as best_agent:
pickle.dump(agents[0], best_agent) | notebooks/prod/n09_dyna_10000_states_full_training.ipynb | mtasende/Machine-Learning-Nanodegree-Capstone | mit |
TensorFlow Lite による芸術的スタイル転送
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://www.tensorflow.org/lite/examples/style_transfer/overview"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
</td>
<td>
<a target="_blank" href="http... | import tensorflow as tf
print(tf.__version__)
import IPython.display as display
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (12,12)
mpl.rcParams['axes.grid'] = False
import numpy as np
import time
import functools | site/ja/lite/examples/style_transfer/overview.ipynb | tensorflow/docs-l10n | apache-2.0 |
コンテンツ画像とスタイル画像、および事前トレーニング済みの TensorFlow Lite モデルをダウンロードします。 | content_path = tf.keras.utils.get_file('belfry.jpg','https://storage.googleapis.com/khanhlvg-public.appspot.com/arbitrary-style-transfer/belfry-2611573_1280.jpg')
style_path = tf.keras.utils.get_file('style23.jpg','https://storage.googleapis.com/khanhlvg-public.appspot.com/arbitrary-style-transfer/style23.jpg')
style_... | site/ja/lite/examples/style_transfer/overview.ipynb | tensorflow/docs-l10n | apache-2.0 |
入力を前処理する
コンテンツ画像とスタイル画像は RGB 画像である必要があります。ピクセル値は [0..1] 間の float32 の数値です。
スタイル画像のサイズは (1, 256, 256, 3) である必要があります。画像を中央でクロップしてサイズを変更します。
コンテンツ画像は (1, 384, 384, 3) である必要があります。画像を中央でクロップしてサイズを変更します。 | # Function to load an image from a file, and add a batch dimension.
def load_img(path_to_img):
img = tf.io.read_file(path_to_img)
img = tf.io.decode_image(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
img = img[tf.newaxis, :]
return img
# Function to pre-process by resizing an central... | site/ja/lite/examples/style_transfer/overview.ipynb | tensorflow/docs-l10n | apache-2.0 |
入力を可視化する | def imshow(image, title=None):
if len(image.shape) > 3:
image = tf.squeeze(image, axis=0)
plt.imshow(image)
if title:
plt.title(title)
plt.subplot(1, 2, 1)
imshow(preprocessed_content_image, 'Content Image')
plt.subplot(1, 2, 2)
imshow(preprocessed_style_image, 'Style Image') | site/ja/lite/examples/style_transfer/overview.ipynb | tensorflow/docs-l10n | apache-2.0 |
TensorFlow Lite でスタイル転送を実行する
スタイルを予測する | # Function to run style prediction on preprocessed style image.
def run_style_predict(preprocessed_style_image):
# Load the model.
interpreter = tf.lite.Interpreter(model_path=style_predict_path)
# Set model input.
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
interpreter.s... | site/ja/lite/examples/style_transfer/overview.ipynb | tensorflow/docs-l10n | apache-2.0 |
スタイルを変換する | # Run style transform on preprocessed style image
def run_style_transform(style_bottleneck, preprocessed_content_image):
# Load the model.
interpreter = tf.lite.Interpreter(model_path=style_transform_path)
# Set model input.
input_details = interpreter.get_input_details()
interpreter.allocate_tensors()
# ... | site/ja/lite/examples/style_transfer/overview.ipynb | tensorflow/docs-l10n | apache-2.0 |
スタイルをブレンドする
コンテンツ画像のスタイルをスタイル化された出力にブレンドさせることができます。こうすると、出力がよりコンテンツ画像のように見えるようになります。 | # Calculate style bottleneck of the content image.
style_bottleneck_content = run_style_predict(
preprocess_image(content_image, 256)
)
# Define content blending ratio between [0..1].
# 0.0: 0% style extracts from content image.
# 1.0: 100% style extracted from content image.
content_blending_ratio = 0.5 #@par... | site/ja/lite/examples/style_transfer/overview.ipynb | tensorflow/docs-l10n | apache-2.0 |
Description
A 100-MVA, 14.4-kV, 0.8-PF-lagging, 50-Hz, two-pole, Y-connected synchronous generator has a per-unit synchronous reactance of 1.1 and a per-unit armature resistance of 0.011. | Vl = 14.4e3 # [V]
S = 100e6 # [VA]
ra = 0.011 # [pu]
xs = 1.1 # [pu]
PF = 0.8
p = 2
fse = 50 # [Hz] | Chapman/Ch4-Problem_4-07.ipynb | dietmarw/EK5312_ElectricalMachines | unlicense |
(a)
What are its synchronous reactance and armature resistance in ohms?
(b)
What is the magnitude of the internal generated voltage $E_A$ at the rated conditions?
What is its torque angle $\delta$ at these conditions?
(c)
Ignoring losses in this generator
What torque must be applied to its shaft by the prime mover... | Vphase_base = Vl / sqrt(3)
print('Vphase_base = {:.0f} V'.format(Vphase_base)) | Chapman/Ch4-Problem_4-07.ipynb | dietmarw/EK5312_ElectricalMachines | unlicense |
Therefore, the base impedance of the generator is:
$$Z_\text{base} = \frac{3V^2_{\phi_\text{base}}}{S_\text{base}}$$ | Zbase = 3*Vphase_base**2 / S
print('Zbase = {:.3f} Ω'.format(Zbase)) | Chapman/Ch4-Problem_4-07.ipynb | dietmarw/EK5312_ElectricalMachines | unlicense |
(b)
The generator impedance in ohms are: | Ra = ra * Zbase
Xs = xs * Zbase
print('''
Ra = {:.4f} Ω Xs = {:.3f} Ω
==============================='''.format(Ra, Xs)) | Chapman/Ch4-Problem_4-07.ipynb | dietmarw/EK5312_ElectricalMachines | unlicense |
(b)
The rated armature current is:
$$I_A = I_L = \frac{S}{\sqrt{3}V_T}$$ | Ia_amp = S / (sqrt(3) * Vl)
print('Ia_amp = {:.0f} A'.format(Ia_amp)) | Chapman/Ch4-Problem_4-07.ipynb | dietmarw/EK5312_ElectricalMachines | unlicense |
The power factor is 0.8 lagging, so: | Ia_angle = -arccos(PF)
Ia = Ia_amp * (cos(Ia_angle) + sin(Ia_angle)*1j)
print('Ia = {:.0f} ∠{:.2f}° A'.format(abs(Ia), Ia_angle / pi *180)) | Chapman/Ch4-Problem_4-07.ipynb | dietmarw/EK5312_ElectricalMachines | unlicense |
It is very often the case that especially in larger machines the armature resistance $R_A$ is simply neclected and one calulates the armature voltage simply as:
$$\vec{E}A = \vec{V}\phi + jX_S\vec{I}_A$$
But since in this case we were given the armature resistance explicitly we should also use it.
Therefore, the intern... | EA = Vphase_base + (Ra + Xs*1j) * Ia
EA_angle = arctan(EA.imag/EA.real)
print('EA = {:.1f} V ∠{:.1f}°'.format(abs(EA), EA_angle/pi*180)) | Chapman/Ch4-Problem_4-07.ipynb | dietmarw/EK5312_ElectricalMachines | unlicense |
Therefore, the magnitude of the internal generated voltage $E_A$ is: | abs(EA) | Chapman/Ch4-Problem_4-07.ipynb | dietmarw/EK5312_ElectricalMachines | unlicense |
V, and the torque angle $\delta$ is: | EA_angle/pi*180 | Chapman/Ch4-Problem_4-07.ipynb | dietmarw/EK5312_ElectricalMachines | unlicense |
degrees.
(c)
Ignoring losses, the input power would equal the output power. Since | Pout = PF * S
print('Pout = {:.1F} MW'.format(Pout/1e6)) | Chapman/Ch4-Problem_4-07.ipynb | dietmarw/EK5312_ElectricalMachines | unlicense |
and,
$$n_\text{sync} = \frac{120f_{se}}{P}$$ | n_sync = 120*fse / p
print('n_sync = {:.0F} r/min'.format(n_sync)) | Chapman/Ch4-Problem_4-07.ipynb | dietmarw/EK5312_ElectricalMachines | unlicense |
the applied torque would be:
$$\tau_\text{app} = \tau_\text{ind} = \frac{P_\text{out}}{\omega_\text{sync}}$$ | w_sync = n_sync * (2*pi/60.0)
tau_app = Pout / w_sync
print('''
τ_app = {:.0f} Nm
================='''.format(tau_app)) | Chapman/Ch4-Problem_4-07.ipynb | dietmarw/EK5312_ElectricalMachines | unlicense |
To access an element in a nested list, first index to the inner list, then index to the item.
Example:
list_of_lists = [[1,2], [3,4], []]
Acess the first index to the inner list and index to the item
python
inner_list = list_of_lists[1] # [3,4]
print inner_list[0] # 3
Or even quicker:
python
list_of_lists[... | sushi_order[0] = 'caterpillar roll'
print(sushi_order) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
TRY IT
Update the last element in prices to be 21.00 and print out the new result | prices[-1] = 21.00
print(prices) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
Operators and Lists
The in operator allows you to see if an element is contained in a list | sushi_order
print(('hamachi' in sushi_order))
if 'otoro' in sushi_order:
print("Big spender!") | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
You can use some arithmatic operators on lists
The + operator concatenates two lists
The * operator duplicates a list that many times | print((sushi_order * 3))
exprep = ['rep'+str(i) for i in range(5)]
exprep
print((prices + sushi_order)) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
Note: You can only concatenate lists with lists! If you want to add a "non-list" element you can use the append() function. | newprices = prices.copy()
newprices.append(22)
print(newprices)
prices | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
Don't forget, you can use the for and in keywords to loop through a list | for item in sushi_order:
print(("I'd like to order the {}.".format(item)))
print("And hold the wasabi!")
for ind, item in enumerate(sushi_order):
print(("I'd like to order the {0} for {1}.".format(item, prices[ind]))) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
TRY IT
Create a variable called lots_of_sushi that repeats the inexpensive list two times | lots_of_sushi = inexpensive*2
print(lots_of_sushi) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
Adding and deleting elements
To add an element to a list, you have a few options
the append method adds an element or elements to the end of a list, if you pass it a list, the next element with be a list (making a list of lists)
the extend method takes a list of elements and adds them all to the end, not creating a... | my_sushis = ['maguro', 'rock n roll']
my_sushis.append('avocado roll')
print(my_sushis)
my_sushis.append(['hamachi', 'california roll'])
print(my_sushis)
my_sushis = ['maguro', 'rock n roll']
my_sushis.extend(['hamachi', 'california roll'])
print(my_sushis) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
TRY IT
Add 'rock n roll' to sushi_order then delete the first element of sushi_order
List Functions
max will return maximum value of list
min returns minimum value of list
sum returns the sum of the values in a list
len returns the number of elements in a list # Just a reminder | numbers = [1, 1, 2, 3, 5, 8]
print((max(numbers)))
print((min(numbers)))
print((sum(numbers)))
print((len(numbers)))
| Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
TRY IT
Find the average of numbers using list functions (and not a loop!) | sum(numbers)/len(numbers) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
Aliasing
If you assign a list to another variable, it will still refer to the same list. This can cause trouble if you change one list because the other will change too. | cooked_rolls = ['unagi roll', 'shrimp tempura roll']
my_order = cooked_rolls
my_order.append('hamachi')
print(my_order)
print(cooked_rolls) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
To check this, you can use the is operator to see if both variable refer to the same object | print((my_order is cooked_rolls)) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
Tuples
Tuples are very similar to lists. The major difference is that tuples are immutable meaning that you can not add, remove, or assign new values to a tuple.
The creator of a tuple is the comma , but by convention people usually surround tuples with parenthesis. | noodles = ('soba', 'udon', 'ramen', 'lo mein', 'somen', 'rice noodle')
print((type(noodles))) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
To create a single element tuple, you need to add a comma to the end of that element (it looks kinda weird) | single_element_tuple = (1,)
print(single_element_tuple)
print((type(single_element_tuple))) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
You can use the indexing and slicing you learned for lists the same with tuples.
But, because tuples are immutable, you cannot use the append, pop, del, extend, or remove methods or even assign new values to indexes | print((noodles[0]))
print((noodles[4:]))
# This should throw an error
noodles[0] = 'spaghetti' | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
You can loop through tuples the same way you loop through lists, using for in | for noodle in noodles:
print(("Yummy, yummy {0} and {1}".format(noodle, 'sushi'))) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
TRY IT
Create a tuple containing 'soy sauce' 'ginger' and 'wasabi' and save it in a variable called accompaniments
Zip
the zip function takes any number of lists of the same length and returns a list of tuples where the tuples will contain the i-th element from each of the lists.
This is really useful when combining li... | print((list(zip([1,2,3], [4,5,6]))))
sushi = ['salmon', 'tuna', 'sea urchin']
prices = [5.5, 6.75, 8]
sushi_and_prices = list(zip(sushi, prices))
sushi_and_prices
for sushi, price in sushi_and_prices:
print(("The {0} costs ${1}".format(sushi, price))) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
Enumerate
While the zip function iterates over two lists, the built-in function enumerate loops through indices and elements of a list. It returns a list of tuples containing the index and value of that element.
for index, value in enumerate(list):
... | exotic_sushi = ['tako', 'toro', 'uni', 'hirame']
for index, item in enumerate(exotic_sushi):
print((index, item)) | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | WomensCodingCircle/CodingCirclePython | mit |
Embarked feature | sns.countplot(data=df, hue="Survived", x="Embarked")
sns.barplot(data=df, x="Embarked", y="Survived")
sns.countplot(data=df, x="Age")
sns.boxplot(data=df, x="Survived", y="Age")
sns.stripplot(
x="Survived", y="Age", data=df, jitter=True, edgecolor="gray", alpha=0.25)
sns.FacetGrid(df, hue="Survived", size=6).ma... | titanic-data-exploration.ipynb | muatik/dm | mit |
The chart above corrects the guess: unfortunatelly, passenger class plays a crucial role. | sns.countplot(data=df[df['Pclass'] == 3], hue="Survived", x="Sex")
sns.barplot(x="Sex", y="Survived", hue="Pclass", data=df);
def titanicFit(df):
X = df[["Sex", "Age", "Pclass", "Embarked"]]
y = df["Survived"]
X.Age.fillna(X.Age.mean(), inplace=True)
X.Sex.replace(to_replace="male", value=1, inplac... | titanic-data-exploration.ipynb | muatik/dm | mit |
Vamos agora criar um modelo baseado nesse conjunto de dados. Vamos utilizar o algoritmo de árvore de decisão para fazer isso. | from sklearn import tree
clf = tree.DecisionTreeClassifier() | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
clf consiste no classificador baseado na árvore de decisão. Precisamos treina-lo com o conjunto da base de dados de treinamento. | clf = clf.fit(features, labels) | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
Observer que o classificador recebe com parâmetro as features e os labels. Esse classificador é um tipo de classificador supervisionado, logo precisa conhecer o "gabarito" das instâncias que estão sendo passadas.
Uma vez que temos o modelo construído, podemos utiliza-lo para classificar uma instância desconhecida. | # Peso 160 e Textura Irregular. Observe que esse tipo de fruta não está presente na base de dados.
print(clf.predict([[160, 0]])) | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
Ele classificou essa fruta como sendo uma Laranja.
HelloWorld++
Vamos estender um pouco mais esse HelloWorld. Claro que o exemplo anterior foi só para passar a idéia de funcionamento de um sistema desse tipo. No entanto, o nosso programa não está aprendendo muita coisa já que a quantidade de exemplos passada para ele é... | from sklearn.datasets import load_iris
dataset_iris = load_iris() | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
Imprimindo as características: | print(dataset_iris.feature_names) | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
Imprimindo os labels: | print(dataset_iris.target_names) | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
Imprimindo os dados: | print(dataset_iris.data)
# Nessa lista, 0 = setosa, 1 = versicolor e 2 = verginica
print(dataset_iris.target) | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
Antes de continuarmos, vale a pena mostrar que o Scikit-Learn exige alguns requisitos para se trabalhar com os dados. Esse tutorial não tem como objetivo fazer um estudo detalhado da biblioteca, mas é importante tomar conhecimento de tais requisitos para entender alguns exemplos que serão mostrados mais à frente. São e... | # Verifique os tipos das features e das classes
print(type(dataset_iris.data))
print(type(dataset_iris.target))
# Verifique o tamanho das features (primeira dimensão = numero de instâncias, segunda dimensão = número de atributos)
print(dataset_iris.data.shape)
# Verifique o tamanho dos labels
print(dataset_iris.targe... | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
Quando importamos a base diretamente do ScikitLearn, as features e labels já vieram em objetos distintos. Só por questão de simplificação dos nomes, vou renomeá-los. | X = dataset_iris.data
Y = dataset_iris.target | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
Construindo e testando um modelo de treinamento
Uma vez que já temos nossa base de dados, o próximo passo é construir nosso modelo de aprendizagem de máquina capaz de utilizar o dataset. No entanto, antes de construirmos nosso modelo é preciso saber qual modelo desenvolver e para isso precisamos definir qual o nosso pr... | import numpy as np
# Determinando os índices que serão retirados da base de treino para formar a base de teste
test_idx = [0, 50, 100] # as instâncias 0, 50 e 100 da base de dados
# Criando a base de treino
train_target = np.delete(dataset_iris.target, test_idx)
train_data = np.delete(dataset_iris.data, test_idx, ... | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
Agora que já temos nosso dataset separado, vamos criar o classificador e treina-lo com os dados de treinamento. | clf = tree.DecisionTreeClassifier()
clf.fit(train_data, train_target) | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
O classificador foi treinado, agora vamos utiliza-lo para classificar as instâncias da base de teste. | print(clf.predict(test_data)) | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
Como estamos trabalhando com o aprendizado supervisionado, podemos comparar com o target que já conhecemos da base de teste. | print(test_target) | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
Observe que, neste caso, nosso classificador teve uma acurácia de 100% acertando todas as instâncias informadas. Claro que esse é só um exemplo e normalmente trabalhamos com valores de acurácias menores que 100%. No entanto, vale ressaltar que para algumas tarefas, como reconhecimento de imagens, as taxas de acurácias ... | from IPython.display import Image
import pydotplus
dot_data = tree.export_graphviz(clf, out_file=None,
feature_names=dataset_iris.feature_names,
class_names=dataset_iris.target_names,
filled=True, rounded=True,
... | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
Observe que nos nós internos pergunta sim ou não para alguma característica. Por exemplo, no nó raiz a pergunta é "pedal width é menor ou igual a 0.8". Isso significa que se a instância que estou querendo classificar possui pedal width menor que 0.8 ela será classificada como setosa. Se isso não for true ela será redir... | print(test_data)
print(test_target) | Introduction/Tutorial01_HelloWorld.ipynb | adolfoguimaraes/machinelearning | mit |
Vertex Training: Distributed Hyperparameter Tuning
<table align="left">
<td>
<a href="https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/hyperparameter_tuning/distributed-hyperparameter-tuning.ipynb"">
<img src="https://cloud.google.com/ml-engine/... | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
! pip3 install {... | notebooks/community/hyperparameter_tuning/distributed-hyperparameter-tuning.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Create a Cloud Storage bucket
The following steps are required, regardless of your notebook environment.
When you submit a custom training job using the Cloud SDK, you will need to provide a staging bucket.
Set the name of your Cloud Storage bucket below. It must be unique across all
Cloud Storage buckets.
You may also... | BUCKET_URI = "gs://[your-bucket-name]" # @param {type:"string"}
REGION = "us-central1" # @param {type:"string"}
if BUCKET_URI == "" or BUCKET_URI is None or BUCKET_URI == "gs://[your-bucket-name]":
BUCKET_URI = "gs://" + PROJECT_ID + "aip-" + TIMESTAMP
print(BUCKET_URI) | notebooks/community/hyperparameter_tuning/distributed-hyperparameter-tuning.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Import libraries and define constants | import os
import sys
from google.cloud import aiplatform
from google.cloud.aiplatform import hyperparameter_tuning as hpt | notebooks/community/hyperparameter_tuning/distributed-hyperparameter-tuning.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Create and run hyperparameter tuning job on Vertex AI
Once your container is pushed to Google Container Registry, you use the Vertex SDK to create and run the hyperparameter tuning job.
You define the following specifications:
* worker_pool_specs: Dictionary specifying the machine type and Docker image. This example de... | worker_pool_specs = [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_T4",
"accelerator_count": 2,
},
"replica_count": 1,
"container_spec": {"image_uri": IMAGE_URI},
}
]
metric_spec = {"accuracy": "maximi... | notebooks/community/hyperparameter_tuning/distributed-hyperparameter-tuning.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Create a CustomJob. | print(BUCKET_URI)
# Create a CustomJob
JOB_NAME = "horses-humans-hyperparam-job" + TIMESTAMP
my_custom_job = aiplatform.CustomJob(
display_name=JOB_NAME,
project=PROJECT_ID,
worker_pool_specs=worker_pool_specs,
staging_bucket=BUCKET_URI,
) | notebooks/community/hyperparameter_tuning/distributed-hyperparameter-tuning.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Then, create and run a HyperparameterTuningJob.
There are a few arguments to note:
max_trial_count: Sets an upper bound on the number of trials the service will run. The recommended practice is to start with a smaller number of trials and get a sense of how impactful your chosen hyperparameters are before scaling up.... | # Create and run HyperparameterTuningJob
hp_job = aiplatform.HyperparameterTuningJob(
display_name=JOB_NAME,
custom_job=my_custom_job,
metric_spec=metric_spec,
parameter_spec=parameter_spec,
max_trial_count=15,
parallel_trial_count=3,
project=PROJECT_ID,
search_algorithm=None,
)
hp_job... | notebooks/community/hyperparameter_tuning/distributed-hyperparameter-tuning.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Click on the generated link in the output to see your run in the Cloud Console. When the job completes, you will see the results of the tuning trials.
Cleaning up
To clean up all Google Cloud resources used in this project, you can delete the Google Cloud
project you used for the tutorial.
Otherwise, you can delete th... | # Set this to true only if you'd like to delete your bucket
delete_bucket = False
if delete_bucket or os.getenv("IS_TESTING"):
! gsutil rm -r $BUCKET_URI | notebooks/community/hyperparameter_tuning/distributed-hyperparameter-tuning.ipynb | GoogleCloudPlatform/vertex-ai-samples | apache-2.0 |
Time stepping | # Init Seismograms
Seismogramm=np.zeros((3,nt)); # Three seismograms
# Calculation of some coefficients
i_dx=1.0/(dx)
kx=np.arange(5,nx-4)
print("Starting time stepping...")
## Time stepping
for n in range(2,nt):
# Inject source wavelet
p[xscr]=p[xscr]+q[n]
# Update velocity
for kx i... | JupyterNotebook/1D/FD_1D_DX8_DT2.ipynb | florianwittkamp/FD_ACOUSTIC | gpl-3.0 |
Save seismograms | ## Save seismograms
np.save("Seismograms/FD_1D_DX8_DT2",Seismogramm)
## Plot seismograms
fig, (ax1, ax2, ax3) = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.4,right=1.6, top = 2 )
ax1.plot(t,Seismogramm[0,:])
ax1.set_title('Seismogram 1')
ax1.set_ylabel('Amplitude')
ax1.set_xlabel('Time in s')
ax1.set_xlim(0, T)
... | JupyterNotebook/1D/FD_1D_DX8_DT2.ipynb | florianwittkamp/FD_ACOUSTIC | gpl-3.0 |
Then we can setup the horsetail matching object, using TP2 from the demo problems as our quantity of interest. Recall this is a fuction that takes two inputs: values of the design variables, x, and values of the uncertain parameters, u, and returns the quantity of interest, q.
Interval uncertainties are given as the t... | from horsetailmatching.demoproblems import TP2
def my_target(h):
return 1
theHM = HorsetailMatching(TP2, u_prob, u_int,
ftarget=(my_target, my_target), samples_prob=n_samples, samples_int=50)
theHM = HorsetailMatching(TP2, prob_uncertainties=[u_prob_alternative], int_uncertainties=[u_... | notebooks/MixedUncertainties.ipynb | lwcook/horsetail-matching | mit |
Note that under mixed uncertainties we can set separate targets for the upper and lower bounds on the CDF (the two horsetail curves) by passing a tuple of (target_for_upper_bound, target_for_lower_bound) to the ftarget argument.
Note also that here we also specified the number of samples to take from the probabilistic ... | print(theHM.evalMetric([2, 3]))
upper, lower, CDFs = theHM.getHorsetail()
(q1, h1, t1) = upper
(q2, h2, t2) = lower
for CDF in CDFs:
plt.plot(CDF[0], CDF[1], c='grey', lw=0.5)
plt.plot(q1, h1, 'b')
plt.plot(q2, h2, 'b')
plt.plot(t1, h1, 'k--')
plt.plot(t2, h2, 'k--')
plt.xlim([0, 15])
plt.ylim([0, 1])
plt.xlabel(... | notebooks/MixedUncertainties.ipynb | lwcook/horsetail-matching | mit |
Since this problem is highly non-linear, we obtain an interestingly shaped horsetail plot with CDFs that cross. Note that the target is plotted in dashed lines. Now to optimize the horsetail matching metric, we simply use the evalMetric method in an optimizer as before: | from scipy.optimize import minimize
solution = minimize(theHM.evalMetric, x0=[1,1], method='Nelder-Mead')
print(solution) | notebooks/MixedUncertainties.ipynb | lwcook/horsetail-matching | mit |
Now we can inspect the horsetail plot of the optimum design by using the getHorsetail method again: | upper, lower, CDFs = theHM.getHorsetail()
for CDF in CDFs:
plt.plot(CDF[0], CDF[1], c='grey', lw=0.5)
plt.plot(upper[0], upper[1], 'r')
plt.plot(lower[0], lower[1], 'r')
plt.plot([theHM.ftarget[0](y) for y in upper[1]], upper[1], 'k--')
plt.plot([theHM.ftarget[1](y) for y in lower[1]], lower[1], 'k--')
plt.xlim([0... | notebooks/MixedUncertainties.ipynb | lwcook/horsetail-matching | mit |
Step 1: Dataset Summary & Exploration
The pickled data is a dictionary with 4 key/value pairs:
'features' is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).
'labels' is a 1D array containing the label/class id of the traffic sign. The file signnames.csv contain... | #Number of original training examples
n_train_ori = X_train_ori.shape[0]
print("Number of original training examples =", n_train_ori)
# Number of training examples after image agumentation
n_train = X_train.shape[0]
print("Number of training examples =", n_train)
# Number of validation examples
n_validation = X_valid... | Traffic_Sign_Classifier.ipynb | rohitbahl1986/TrafficSignClassifier | mit |
Include an exploratory visualization of the dataset | ### Data exploration visualization
# Visualizations will be shown in the notebook.
%matplotlib inline
def plotTrafficSign(n_rows, n_cols):
"""
This function displays random images from the trainign data set.
"""
fig, axes = plt.subplots(nrows = n_rows, ncols = n_cols, figsize=(60,30))
for row in a... | Traffic_Sign_Classifier.ipynb | rohitbahl1986/TrafficSignClassifier | mit |
Histogram of the data shows that the trainign data is unevenly distributed. This might affect the training of CNN model.
Comparing the distribution across the 3 sets (training/validation/test), it seems that the distribution is similar in all the sets.
Step 2: Design and Test a Model Architecture
Design and implement ... | ### Preprocess the data.
def dataGeneration():
"""
This function auguments the training data by creating new data (via image rotation)
"""
global X_train
global y_train
global y_train_ori
global X_train_ori
global n_train_ori
#Create new data by fliping the images in the vertic... | Traffic_Sign_Classifier.ipynb | rohitbahl1986/TrafficSignClassifier | mit |
Model Architecture | def LeNet(x, keep_prob=1.0):
# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
mu = 0
sigma = 0.1
global n_classes
# Layer 1: Convolutional. Input = 32x32x3. Output = 28x28x6.
conv1_W = tf.Variable(tf.truncated_normal(shape=(5, ... | Traffic_Sign_Classifier.ipynb | rohitbahl1986/TrafficSignClassifier | mit |
Train, Validate and Test the Model
A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation
sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting. | #Model training
class CEvaluate:
def __init__(self, learning_rate=0.001, epoch=10, batch_size=128):
self.input_conv = tf.placeholder(tf.float32, (None, 32, 32, 3))
self.target = tf.placeholder(tf.int32, (None))
self.keep_prob = tf.placeholder(tf.float32)
self.model = CModel... | Traffic_Sign_Classifier.ipynb | rohitbahl1986/TrafficSignClassifier | mit |
Step 3: Test a Model on New Images
To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.
You may find signnames.csv useful as it contains mappings from the class id (integer) to the actual s... | ### Load the images and plot them.
import os
test_images = os.listdir('test_images')
num_test_images = 5
X_new_test = np.empty((num_test_images, 32, 32, 3))
y_new_test = np.empty(num_test_images)
dic = {"60.jpg":3, "70.jpg":4, "roadwork.jpg":25, "stop.jpg":14, "yield.jpg":13}
for index, image_name in enumerate(test_ima... | Traffic_Sign_Classifier.ipynb | rohitbahl1986/TrafficSignClassifier | mit |
Predict the Sign Type for Each Image/Analyze Performance/ Output Soft Max | with open('signnames.csv', mode='r') as file:
reader = csv.reader(file)
sign_mapping = {rows[0]:rows[1] for rows in reader}
X_new_test = normalize(X_new_test)
predict, top_k_softmax = eval_model.predictions(X_new_test)
for output,expected in zip(predict,y_new_test):
print("Expected {} ...... Output {}".fo... | Traffic_Sign_Classifier.ipynb | rohitbahl1986/TrafficSignClassifier | mit |
Output Top 5 Softmax Probabilities For Each Image Found on the Web | print("top_k_softmax == ", top_k_softmax) | Traffic_Sign_Classifier.ipynb | rohitbahl1986/TrafficSignClassifier | mit |
2) What are all the different book categories the NYT ranked in June 6, 2009? How about June 6, 2015? | # STEP 1: Exploring the data structure using just one of the dates from the question
bookcat_response = requests.get('http://api.nytimes.com/svc/books/v2/lists/names.json?published-date=2009-06-06&api-key=0c3ba2a8848c44eea6a3443a17e57448')
bookcat_data = bookcat_response.json()
print(type(bookcat_data))
print(bookcat_d... | foundations-homework/05/.ipynb_checkpoints/homework-05-gruen-nyt-checkpoint.ipynb | gcgruen/homework | mit |
3) Muammar Gaddafi's name can be transliterated many many ways. His last name is often a source of a million and one versions - Gadafi, Gaddafi, Kadafi, and Qaddafi to name a few. How many times has the New York Times referred to him by each of those names?
Tip: Add "Libya" to your search to make sure (-ish) you're tal... | # STEP 1a: EXPLORING THE DATA
test_response = requests.get('http://api.nytimes.com/svc/search/v2/articlesearch.json?q=Gaddafi+Libya&api-key=0c3ba2a8848c44eea6a3443a17e57448')
test_data = test_response.json()
print(type(test_data))
print(test_data.keys())
test_hits = test_data['response']
print(type(test_hits))
print(... | foundations-homework/05/.ipynb_checkpoints/homework-05-gruen-nyt-checkpoint.ipynb | gcgruen/homework | mit |
1.1 Subset the dataset into the moderate variable levels
In order to verifify whether the moderator variabel, urbanrate, plays a role into the interaction between incomeperperon and lifeexpectancy, we'll subset our dataset into two groups: onde group for countries below 50% of urbanrate population, and the other group ... | # Dataset with low urban rate.
df_low = df[df.urbanrate < 50]
# Dataset with high urban rate.
df_high = df[df.urbanrate >= 50] | Week_4.ipynb | srodriguex/coursera_data_analysis_tools | mit |
1.2 Pearson correlation $r$
For each subset, we'll conduct the Pearson correlation analysis and verify the results. | r_low = pearsonr(df_low.incomeperperson, df_low.lifeexpectancy)
r_high = pearsonr(df_high.incomeperperson, df_high.lifeexpectancy)
print('Correlation in LOW urban rate: {}'.format(r_low))
print('Correlation in HIGH urban rate: {}'.format(r_high))
print('Percentage of variability LOW urban rate: {:2}%'.
format(... | Week_4.ipynb | srodriguex/coursera_data_analysis_tools | mit |
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