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The document discusses random numbers and random walks. It introduces random number generators in NumPy and simulates coin tosses and random walks by randomly generating 0s and 1s. It shows how running a random walk simulation multiple times produces a distribution, which can be visualized using a histogram. Running the simulation more times improves the approximation of the true distribution.
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When running the code below I am getting some errors (see image)- The.docx
When running the code below I am getting some errors (see image). The line, in the code below, that the error is coming from will be highlighted in bold . Any help fixing it would be appreciated.
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
#1) Generate the synthetic data using the following Python code snippet.
# Generate synthetic data
N = 100
# Zeros form a Gaussian centered at (-1, -1)
x_zeros = np.random.multivariate_normal(mean=np.array((-1, -1)), cov=.1*np.eye(2), size=(N//2,))
y_zeros = np.zeros((N//2,))
# Ones form a Gaussian centered at (1, 1)
x_ones = np.random.multivariate_normal(mean=np.array((1, 1)), cov=.1*np.eye(2), size=(N//2,))
y_ones = np.ones((N//2,))
x_np = np.vstack([x_zeros, x_ones])
y_np = np.concatenate([y_zeros, y_ones])
# Plot x_zeros and x_ones on the same graph
plt.scatter(x_zeros[:,0], x_zeros[:,1], label='class 0')
plt.scatter(x_ones[:,0], x_ones[:,1], label='class 1')
plt.legend()
plt.show()
#3) Generate a TensorFlow graph.
with tf.name_scope("placeholders"):
x = tf.constant(x_np, dtype=tf.float32)
y = tf.constant(y_np, dtype=tf.float32)
with tf.name_scope("weights"):
W = tf.Variable(tf.random.normal((2, 1)))
b = tf.Variable(tf.random.normal((1,)))
with tf.name_scope("prediction"):
y_logit = tf.squeeze(tf.matmul(x, W) + b)
# the sigmoid gives the class probability of 1
y_one_prob = tf.sigmoid(y_logit)
# Rounding P(y=1) will give the correct prediction.
y_pred = tf.round(y_one_prob)
with tf.name_scope("loss"):
# Compute the cross-entropy term for each datapoint
entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=y_logit, labels=y)
# Sum all contributions
l = tf.reduce_sum(entropy)
with tf.name_scope("optim"):
train_op = tf.compat.v1.train.AdamOptimizer(.01).minimize(l)
with tf.name_scope("summaries"):
tf.compat.v1.summary.scalar("loss", l)
merged = tf.compat.v1.summary.merge_all()
train_writer = tf.compat.v1.summary.FileWriter('logistic-train', tf.compat.v1.get_default_graph())
#4) Train the model, get the weights, and make predictions.
with tf.compat.v1.Session() as sess:
# Initialize all variables
sess.run(tf.compat.v1.global_variables_initializer())
# Train the model for 100 epochs
for epoch in range(100):
# Run the train_op
_, summary, loss = sess.run([train_op, merged, l])
print("Epoch:", epoch, "Loss:", loss)
# Write the summary for TensorBoard
train_writer.add_summary(summary, epoch)
# Get the weights and biases
W_final, b_final = sess.run([W, b])
# Get the predictions
y_pred_np = sess.run(y_pred)
#5) Plot the predicted outputs on top of the data.
plt.scatter(x_np[y_pred_np==0,0], x_np[y_pred_np==0,1], label='class 0')
plt.scatter(x_np[y_pred_np==1,0], x_np[y_pred_np==1,1], label='class 1')
plt.legend()
plt.show()
.
functions2-200924082810.pdf
This document discusses random functions in Python. It explains how to import the random module and describes functions like random(), randrange(), and randint() to generate random floats, integers within a range, and random selection from lists. random() generates a random number between 0 and 1, while randrange() and randint() are used to get random integers within a specified range. Examples are provided to demonstrate how to generate random numbers between certain values and with certain step increments.
FUNCTIONS IN PYTHON[RANDOM FUNCTION]
This document discusses random functions in Python. It explains how to import the random module and describes several functions:
- random() generates random float numbers between 0 and 1
- randrange() returns random integers within a given range
- randint() returns random integers within a range similar to randrange()
Examples are provided to demonstrate how to use these functions to generate random numbers between certain values or in lists.
Please help fill in the missing code below in order for it run correct.docx
The document describes code for generating synthetic classification data and building a logistic regression model in TensorFlow to classify the data. It generates data points from two Gaussian distributions, plots the data, builds the TensorFlow graph with placeholders, weights, predictions, loss and training operation. It is missing code to actually train the model, extract the trained weights to make predictions, and plot the predictions over the original data.
Data Handling.pdf
This document discusses data types in Python. It describes the main numeric, string, boolean, and container data types. For numeric types, it explains integers, floating point numbers, complex numbers, and how to convert between them. For strings, it demonstrates indexing, slicing, concatenation and iteration. It also discusses booleans, lists, tuples, sets and dictionaries. The document then covers operators in Python like arithmetic, comparison, logical and bitwise operators. It provides examples of using each operator and precedence rules. Finally, it briefly discusses expressions and type conversion.
Statistical inference for (Python) Data Analysis. An introduction.
A brief showcase on how Python can help perform statistical inference. Few examples with a short library overview.
PY_17_06_20-1.pptx
The document discusses various random number and string functions in Python. It includes:
1) Random number functions like choice(), randrange(), and random() that can generate random integers, floats, or elements from a list/string.
2) Built-in string methods like capitalize(), count(), encode/decode(), and find() that allow manipulating and searching strings.
3) Examples of using these functions and methods on sample strings and numbers.
A/B Testing for Game Design
1. The document discusses A/B testing approaches for game design, noting key areas that can be tested like onboarding experiences, monetization strategies, and retention mechanics.
2. It introduces Bayesian approaches to A/B testing, noting that observing results allows updating beliefs about hypotheses rather than relying on passing a threshold for significance.
3. Key challenges with frequentist approaches are discussed like multiple comparisons inflating false positive rates, and "peeking" at intermediate results invalidating conclusions. Bayesian methods account for uncertainty and can incorporate prior information and new evidence iteratively.
Recommended
When running the code below I am getting some errors (see image)- The.docx
When running the code below I am getting some errors (see image). The line, in the code below, that the error is coming from will be highlighted in bold . Any help fixing it would be appreciated.
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
#1) Generate the synthetic data using the following Python code snippet.
# Generate synthetic data
N = 100
# Zeros form a Gaussian centered at (-1, -1)
x_zeros = np.random.multivariate_normal(mean=np.array((-1, -1)), cov=.1*np.eye(2), size=(N//2,))
y_zeros = np.zeros((N//2,))
# Ones form a Gaussian centered at (1, 1)
x_ones = np.random.multivariate_normal(mean=np.array((1, 1)), cov=.1*np.eye(2), size=(N//2,))
y_ones = np.ones((N//2,))
x_np = np.vstack([x_zeros, x_ones])
y_np = np.concatenate([y_zeros, y_ones])
# Plot x_zeros and x_ones on the same graph
plt.scatter(x_zeros[:,0], x_zeros[:,1], label='class 0')
plt.scatter(x_ones[:,0], x_ones[:,1], label='class 1')
plt.legend()
plt.show()
#3) Generate a TensorFlow graph.
with tf.name_scope("placeholders"):
x = tf.constant(x_np, dtype=tf.float32)
y = tf.constant(y_np, dtype=tf.float32)
with tf.name_scope("weights"):
W = tf.Variable(tf.random.normal((2, 1)))
b = tf.Variable(tf.random.normal((1,)))
with tf.name_scope("prediction"):
y_logit = tf.squeeze(tf.matmul(x, W) + b)
# the sigmoid gives the class probability of 1
y_one_prob = tf.sigmoid(y_logit)
# Rounding P(y=1) will give the correct prediction.
y_pred = tf.round(y_one_prob)
with tf.name_scope("loss"):
# Compute the cross-entropy term for each datapoint
entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=y_logit, labels=y)
# Sum all contributions
l = tf.reduce_sum(entropy)
with tf.name_scope("optim"):
train_op = tf.compat.v1.train.AdamOptimizer(.01).minimize(l)
with tf.name_scope("summaries"):
tf.compat.v1.summary.scalar("loss", l)
merged = tf.compat.v1.summary.merge_all()
train_writer = tf.compat.v1.summary.FileWriter('logistic-train', tf.compat.v1.get_default_graph())
#4) Train the model, get the weights, and make predictions.
with tf.compat.v1.Session() as sess:
# Initialize all variables
sess.run(tf.compat.v1.global_variables_initializer())
# Train the model for 100 epochs
for epoch in range(100):
# Run the train_op
_, summary, loss = sess.run([train_op, merged, l])
print("Epoch:", epoch, "Loss:", loss)
# Write the summary for TensorBoard
train_writer.add_summary(summary, epoch)
# Get the weights and biases
W_final, b_final = sess.run([W, b])
# Get the predictions
y_pred_np = sess.run(y_pred)
#5) Plot the predicted outputs on top of the data.
plt.scatter(x_np[y_pred_np==0,0], x_np[y_pred_np==0,1], label='class 0')
plt.scatter(x_np[y_pred_np==1,0], x_np[y_pred_np==1,1], label='class 1')
plt.legend()
plt.show()
.
functions2-200924082810.pdf
This document discusses random functions in Python. It explains how to import the random module and describes functions like random(), randrange(), and randint() to generate random floats, integers within a range, and random selection from lists. random() generates a random number between 0 and 1, while randrange() and randint() are used to get random integers within a specified range. Examples are provided to demonstrate how to generate random numbers between certain values and with certain step increments.
FUNCTIONS IN PYTHON[RANDOM FUNCTION]
This document discusses random functions in Python. It explains how to import the random module and describes several functions:
- random() generates random float numbers between 0 and 1
- randrange() returns random integers within a given range
- randint() returns random integers within a range similar to randrange()
Examples are provided to demonstrate how to use these functions to generate random numbers between certain values or in lists.
Please help fill in the missing code below in order for it run correct.docx
The document describes code for generating synthetic classification data and building a logistic regression model in TensorFlow to classify the data. It generates data points from two Gaussian distributions, plots the data, builds the TensorFlow graph with placeholders, weights, predictions, loss and training operation. It is missing code to actually train the model, extract the trained weights to make predictions, and plot the predictions over the original data.
Data Handling.pdf
This document discusses data types in Python. It describes the main numeric, string, boolean, and container data types. For numeric types, it explains integers, floating point numbers, complex numbers, and how to convert between them. For strings, it demonstrates indexing, slicing, concatenation and iteration. It also discusses booleans, lists, tuples, sets and dictionaries. The document then covers operators in Python like arithmetic, comparison, logical and bitwise operators. It provides examples of using each operator and precedence rules. Finally, it briefly discusses expressions and type conversion.
Statistical inference for (Python) Data Analysis. An introduction.
A brief showcase on how Python can help perform statistical inference. Few examples with a short library overview.
PY_17_06_20-1.pptx
The document discusses various random number and string functions in Python. It includes:
1) Random number functions like choice(), randrange(), and random() that can generate random integers, floats, or elements from a list/string.
2) Built-in string methods like capitalize(), count(), encode/decode(), and find() that allow manipulating and searching strings.
3) Examples of using these functions and methods on sample strings and numbers.
A/B Testing for Game Design
1. The document discusses A/B testing approaches for game design, noting key areas that can be tested like onboarding experiences, monetization strategies, and retention mechanics.
2. It introduces Bayesian approaches to A/B testing, noting that observing results allows updating beliefs about hypotheses rather than relying on passing a threshold for significance.
3. Key challenges with frequentist approaches are discussed like multiple comparisons inflating false positive rates, and "peeking" at intermediate results invalidating conclusions. Bayesian methods account for uncertainty and can incorporate prior information and new evidence iteratively.
error 2.pdf101316, 6(46 PM01_errorPage 1 of 5http.docx
error 2.pdf
10/13/16, 6(46 PM01_error
Page 1 of 5http://localhost:8888/nbconvert/html/group/01_error.ipynb?download=false
In [ ]: %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import sys
Error Definitions
Following is an example for the concept of absolute error, relative error and decimal precision:
We shall test the approximation to common mathematical constant, . Compute the absolute and relative
errors along with the decimal precision if we take the approximate value of .
In [ ]: # We can use the formulas you derieved above to calculate the actual n
umbers
absolute_error = np.abs(np.exp(1) - 2.718)
relative_error = absolute_error/np.exp(1)
print "The absolute error is "+str(absolute_error)
print "The relative error is "+str(relative_error)
Machine epsilon is a very important concept in floating point error. The value, even though miniscule, can
easily compund over a period to cause huge problems.
Below we see a problem demonstating how easily machine error can creep into a simple piece of code:
In [ ]: a = 4.0/3.0
b = a - 1.0
c = 3*b
eps = 1 - c
print 'Value of a is ' +str(a)
print 'Value of b is ' +str(b)
print 'Value of c is ' +str(c)
print 'Value of epsilon is ' +str(eps)
Ideally eps should be 0, but instead we see the machine epsilon and while the value is small it can lead to
issues.
e
e = 2.718
10/13/16, 6(46 PM01_error
Page 2 of 5http://localhost:8888/nbconvert/html/group/01_error.ipynb?download=false
In [ ]: print "The progression of error:"
for i in range(1,20):
print str(abs((10**i)*c - (10**i)))
The largest floating point number
The formula for obtaining the number is shown below, instead of calculating the value we can use the
system library to find this value.
In [ ]: maximum = (2.0-eps)*2.0**1023
print sys.float_info.max
print 'Value of maximum is ' +str(maximum)
The smallest floating point number
The formula for obtaining the number is shown below. Similarly the value can be found using the system
library to find this value.
In [ ]: minimum = eps*2.0**(-1022)
print sys.float_info.min
print sys.float_info.min*sys.float_info.epsilon
print 'Value of minimum is ' +str(minimum)
As we try to compute a number bigger than the aforementioned, largest floating point number we see weird
errors. The computer assigns infinity to these values.
In [ ]: overflow = maximum*10.0
print 'Value of overflow is ' +str(overflow)
As we try to compute a number smaller than the aforementioned smallest floating point number we see that
the computer assigns it the value 0. We actually lose precision in this case.
10/13/16, 6(46 PM01_error
Page 3 of 5http://localhost:8888/nbconvert/html/group/01_error.ipynb?download=false
In [1]: underflow = minimum/2.0
print 'Value of underflow is ' +str(underflow)
Truncation error is a very common form of error you will keep seing in the area of Numerical
Analysis/Computing.
Here we will look at the classic Calculus example of the approximation near 0. We c ...
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一比一原版美国亚利桑那大学毕业证（ua毕业证书）如何办理
一模一样【微信：A575476】【美国亚利桑那大学毕业证（ua毕业证书）成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
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2:同时对留学生所学专业登记给予评定
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我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
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选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(QUT毕业证)昆士兰科技大学毕业证成绩单如何办理
QUT毕业证【微信95270640】办理QUT毕业证【Q微信95270640】昆士兰科技大学毕业证书原版↑制作昆士兰科技大学学历认证文凭办理昆士兰科技大学留信网认证,留学回国办理毕业证成绩单文凭学历认证【Q微信95270640】专业为海外学子办理毕业证成绩单、文凭制作，学历仿制，回国人员证明、做文凭，研究生、本科、硕士学历认证、留信认证、结业证、学位证书样本、美国教育部认证百分百真实存档可查】
全套服务：昆士兰科技大学昆士兰科技大学毕业证offer真实回国人员证明 #真实教育部认证。让您回国发展信心十足#铸就十年品质！信誉！实体公司！可以视频看办公环境样板如需办理真实可查可以先到公司面谈勿轻信小中介黑作坊！
可以提供昆士兰科技大学钢印 #水印 #烫金 #激光防伪 #凹凸版 #最新版的毕业证 #百分之百让您绝对满意
印刷DHL快递毕业证 #成绩单7个工作日真实大使馆教育部认证1个月。为了达到高水准高效率
请您先以qq或微信的方式对我们的服务进行了解后如果有昆士兰科技大学昆士兰科技大学毕业证offer帮助再进行电话咨询。
国外毕业证学位证成绩单如何办理：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作昆士兰科技大学毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。望着父亲山娃反问道父亲听了并不回答只是吃吃地笑山娃很精神越逛越起劲父亲却越逛越疲倦望着父亲呵欠连天的样子山娃也说困了累了回家吧小屋闷罐一般头顶上的三叶扇彻夜呜呜作响搅得满屋热气腾腾也搅得山娃心烦意乱父亲一上床就呼呼大睡山娃却辗转反侧睡不着山娃一次又一次摸索着爬起来一遍又一遍地用暖乎乎的冷水擦身往水泥地板上一勺一勺的洒水也不知过了多久山娃竟迷迷糊糊地睡着了迷迷糊糊地又闻到了闹钟刺耳的铃声和哐咣的关和
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In [ ]: %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import sys
Error Definitions
Following is an example for the concept of absolute error, relative error and decimal precision:
We shall test the approximation to common mathematical constant, . Compute the absolute and relative
errors along with the decimal precision if we take the approximate value of .
In [ ]: # We can use the formulas you derieved above to calculate the actual n
umbers
absolute_error = np.abs(np.exp(1) - 2.718)
relative_error = absolute_error/np.exp(1)
print "The absolute error is "+str(absolute_error)
print "The relative error is "+str(relative_error)
Machine epsilon is a very important concept in floating point error. The value, even though miniscule, can
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Below we see a problem demonstating how easily machine error can creep into a simple piece of code:
In [ ]: a = 4.0/3.0
b = a - 1.0
c = 3*b
eps = 1 - c
print 'Value of a is ' +str(a)
print 'Value of b is ' +str(b)
print 'Value of c is ' +str(c)
print 'Value of epsilon is ' +str(eps)
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issues.
e
e = 2.718
10/13/16, 6(46 PM01_error
Page 2 of 5http://localhost:8888/nbconvert/html/group/01_error.ipynb?download=false
In [ ]: print "The progression of error:"
for i in range(1,20):
print str(abs((10**i)*c - (10**i)))
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The formula for obtaining the number is shown below, instead of calculating the value we can use the
system library to find this value.
In [ ]: maximum = (2.0-eps)*2.0**1023
print sys.float_info.max
print 'Value of maximum is ' +str(maximum)
The smallest floating point number
The formula for obtaining the number is shown below. Similarly the value can be found using the system
library to find this value.
In [ ]: minimum = eps*2.0**(-1022)
print sys.float_info.min
print sys.float_info.min*sys.float_info.epsilon
print 'Value of minimum is ' +str(minimum)
As we try to compute a number bigger than the aforementioned, largest floating point number we see weird
errors. The computer assigns infinity to these values.
In [ ]: overflow = maximum*10.0
print 'Value of overflow is ' +str(overflow)
As we try to compute a number smaller than the aforementioned smallest floating point number we see that
the computer assigns it the value 0. We actually lose precision in this case.
10/13/16, 6(46 PM01_error
Page 3 of 5http://localhost:8888/nbconvert/html/group/01_error.ipynb?download=false
In [1]: underflow = minimum/2.0
print 'Value of underflow is ' +str(underflow)
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This document announces the winners of the 2024 Youth Poster Contest organized by MATFORCE. It lists the grand prize and age category winners for grades K-6, 7-12, and individual age groups from 5 years old to 18 years old.
Heart Touching Romantic Love Shayari In English with Images
Explore our beautiful collection of Romantic Love Shayari in English to express your love. These heartfelt shayaris are perfect for sharing with your loved one. Get the best words to show your love and care.
❼❷⓿❺❻❷❽❷❼❽ Dpboss Matka ! Fix Satta Matka ! Matka Result ! Matka Guessing ! ...
❼❷⓿❺❻❷❽❷❼❽ Dpboss Matka ! Fix Satta Matka ! Matka Result ! Matka Guessing ! ...❼❷⓿❺❻❷❽❷❼❽ Dpboss Kalyan Satta Matka Guessing Matka Result Main Bazar chart
❼❷⓿❺❻❷❽❷❼❽ Dpboss Matka ! Fix Satta Matka ! Matka Result ! Matka Guessing ! Final Matka ! Matka Result ! Dpboss Matka ! Matka Guessing ! Satta Matta Matka 143 ! Kalyan Matka ! Satta Matka Fast Result ! Kalyan Matka Guessing ! Dpboss Matka Guessing ! Satta 143 ! Kalyan Chart ! Kalyan final ! Satta guessing ! Matka tips ! Matka 143 ! India Matka ! Matka 420 ! matka Mumbai ! Satta chart ! Indian Satta ! Satta King ! Satta 143 ! Satta batta ! Satta मटका ! Satta chart ! Matka 143 ! Matka Satta ! India Matka ! Indian Satta Matka ! Final ankCherries 32 collection of colorful paintings
The cherry: beauty, softness, its heart-shaped plastic has inspired artists since Antiquity. Cherries and strawberries were considered the fruits of paradise and thus represented the souls of men.
Fashionista Chic Couture Mazes and Coloring AdventureA
Fashionista Chic Couture Maze & Coloring Adventures is a coloring and activity book filled with many maze games and coloring activities designed to delight and engage young fashion enthusiasts. Each page offers a unique blend of fashion-themed mazes and stylish illustrations to color, inspiring creativity and problem-solving skills in children.
➒➌➎➏➑➐➋➑➐➐ Dpboss Satta Matka Matka Guessing Kalyan Chart Indian Matka Satta ...
➒➌➎➏➑➐➋➑➐➐ Dpboss Satta Matka Matka Guessing Kalyan Chart Indian Matka Satta ...➒➌➎➏➑➐➋➑➐➐Dpboss Matka Guessing Satta Matka Kalyan Chart Indian Matka
➒➌➎➏➑➐➋➑➐➐ Dpboss Satta Matta Matka Kalyan Chart Indian Matka Dpboss Matka Kalyan panel Chart Matka Guessing Satta Matka 一比一原版美国加州大学圣地亚哥分校毕业证（ucsd毕业证书）如何办理
一模一样【微信：A575476】【美国加州大学圣地亚哥分校毕业证（ucsd毕业证书）成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Ealing London Independent Photography meeting - June 2024
Photographs from trip to American Deep South
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storyboard: Victor and Verlin discussing about top hat
storyboard: Victor and Verlin discussing about top hat
In Focus_ The Evolution of Boudoir Photography in NYC.pdf
In Focus_ The Evolution of Boudoir Photography in NYC.pdf
All the images mentioned in 'See What You're Missing'
All the images mentioned in 'See What You're Missing'
Heart Touching Romantic Love Shayari In English with Images
Heart Touching Romantic Love Shayari In English with Images
❼❷⓿❺❻❷❽❷❼❽ Dpboss Matka ! Fix Satta Matka ! Matka Result ! Matka Guessing ! ...
❼❷⓿❺❻❷❽❷❼❽ Dpboss Matka ! Fix Satta Matka ! Matka Result ! Matka Guessing ! ...
Fashionista Chic Couture Mazes and Coloring AdventureA
Fashionista Chic Couture Mazes and Coloring AdventureA
➒➌➎➏➑➐➋➑➐➐ Dpboss Satta Matka Matka Guessing Kalyan Chart Indian Matka Satta ...
➒➌➎➏➑➐➋➑➐➐ Dpboss Satta Matka Matka Guessing Kalyan Chart Indian Matka Satta ...
Ealing London Independent Photography meeting - June 2024
Ealing London Independent Photography meeting - June 2024
chapter5.pdf
- 1. Random Numbers INTERMEDIATE PYTHON Hugo Bowne-Anderson Data Scientist at DataCamp
- 7. INTERMEDIATE PYTHON Can't go below step 0 0.1 % chance of falling down the stairs Bet: you'll reach step 60
- 8. INTERMEDIATE PYTHON How to solve? Analytical Simulate the process Hacker statistics!
- 9. INTERMEDIATE PYTHON Random generators import numpy as np np.random.rand() # Pseudo-random numbers 0.9535543896720104 # Mathematical formula np.random.seed(123) # Starting from a seed np.random.rand() 0.6964691855978616 np.random.rand() 0.28613933495037946
- 10. INTERMEDIATE PYTHON Random generators np.random.seed(123) np.random.rand() 0.696469185597861 # Same seed: same random numbers! np.random.rand() # Ensures "reproducibility" 0.28613933495037946
- 11. INTERMEDIATE PYTHON Coin toss game.py import numpy as np np.random.seed(123) coin = np.random.randint(0,2) # Randomly generate 0 or 1 print(coin) 0
- 12. INTERMEDIATE PYTHON Coin toss game.py import numpy as np np.random.seed(123) coin = np.random.randint(0,2) # Randomly generate 0 or 1 print(coin) if coin == 0: print("heads") else: print("tails") 0 heads
- 14. Random Walk INTERMEDIATE PYTHON Hugo Bowne-Anderson Data Scientist at DataCamp
- 16. INTERMEDIATE PYTHON Random Walk Known in Science Path of molecules Gambler's nancial status
- 17. INTERMEDIATE PYTHON Heads or Tails headtails.py import numpy as np np.random.seed(123) outcomes = [] for x in range(10) : coin = np.random.randint(0, 2) if coin == 0 : outcomes.append("heads") else : outcomes.append("tails") print(outcomes) ['heads', 'tails', 'heads', 'heads', 'heads', 'heads', 'heads', 'tails', 'tails', 'heads']
- 18. INTERMEDIATE PYTHON Heads or Tails: Random Walk headtailsrw.py import numpy as np np.random.seed(123) tails = [0] for x in range(10) : coin = np.random.randint(0, 2) tails.append(tails[x] + coin) print(tails) [0, 0, 1, 1, 1, 1, 1, 1, 2, 3, 3]
- 19. INTERMEDIATE PYTHON Step to Walk outcomes ['heads', 'tails', 'heads', 'heads', 'heads', 'heads', 'heads', 'tails', 'tails', 'heads'] tails [0, 0, 1, 1, 1, 1, 1, 1, 2, 3, 3]
- 21. Distribution INTERMEDIATE PYTHON Hugo Bowne-Anderson Data Scientist at DataCamp
- 23. INTERMEDIATE PYTHON Random Walk headtailsrw.py import numpy as np np.random.seed(123) tails = [0] for x in range(10) : coin = np.random.randint(0, 2) tails.append(tails[x] + coin)
- 24. INTERMEDIATE PYTHON 100 runs distribution.py import numpy as np np.random.seed(123) final_tails = [] for x in range(100) : tails = [0] for x in range(10) : coin = np.random.randint(0, 2) tails.append(tails[x] + coin) final_tails.append(tails[-1]) print(final_tails) [3, 6, 4, 5, 4, 5, 3, 5, 4, 6, 6, 8, 6, 4, 7, 5, 7, 4, 3, 3, ..., 4]
- 25. INTERMEDIATE PYTHON Histogram, 100 runs distribution.py import numpy as np import matplotlib.pyplot as plt np.random.seed(123) final_tails = [] for x in range(100) : tails = [0] for x in range(10) : coin = np.random.randint(0, 2) tails.append(tails[x] + coin) final_tails.append(tails[-1]) plt.hist(final_tails, bins = 10) plot.show()
- 26. INTERMEDIATE PYTHON Histogram, 100 runs
- 27. INTERMEDIATE PYTHON Histogram, 1,000 runs distribution.py import numpy as np import matplotlib.pyplot as plt np.random.seed(123) final_tails = [] for x in range(1000) : # <-- tails = [0] for x in range(10) : coin = np.random.randint(0, 2) tails.append(tails[x] + coin) final_tails.append(tails[-1]) plt.hist(final_tails, bins = 10) plot.show()
- 28. INTERMEDIATE PYTHON Histogram, 1,000 runs
- 29. INTERMEDIATE PYTHON Histogram, 10,000 runs distribution.py import numpy as np import matplotlib.pyplot as plt np.random.seed(123) final_tails = [] for x in range(10000) : # <-- tails = [0] for x in range(10) : coin = np.random.randint(0, 2) tails.append(tails[x] + coin) final_tails.append(tails[-1]) plt.hist(final_tails, bins = 10) plot.show()