initial commit

add url to tutorial

Create README.md

linear regression tutorial

update a comment for linear regression

add Classifier class and change main() to test()

rename classifier to regressor. oops!

add line of best fit for absolute error

add k-means example

fix bug and add link to tutorial

update readme

add bayes classifier

add knn

add tutorial links

matricize linear regression

matricize regressor also

matricize regressor also

add predict and fit to regressor

add logistic regression for mnist

add tutorial link logistic

add linear regression 1-d class code, add GMM for some reason was not added yet

oops, now add GMM

add r-squared

add 2d and poly examples

wip logistic regression class

add course urls

ann examples

add class links

try batch on donut problem ann

oops

more oopses

change slow code too

nlp class

fix lsa

add url

more links

misc stuff

oops

add new class

update comment

add gpu script

add course links

add one more

i forget

cnn

update cnn class

add unsupervised class

oops

erase unneeded line

use eye

renet test

fix reshaping

add GRU

add real mnist

add batches and downcast

add all scan version

unsupervised deep learning

updates

visualize features

add class url

fix load and save

use get_value instead of eval

visualize better

e-commerce example

visualize lda

ann updates

update tf

update deep unsupervised + a test

minor unsupervised fix

some wip files

add lstm wiki + minor fixes

compartmentalize gru,lstm add decent embeddings

add visualize embeddings

add vanishing gradient demo and xwing

30 epochs

add url to rnn

rrnn variable learning rate

add linear regression examples

wip

add word2vec and glove

update

remove some unnecessary comments

add rnn init

cnn additions

rntn

update util

print out

remove divide by len labels, only look at root for scoring

extra comment

increment j

recursive nn tensorflow

faster train score rntn

add theano renn and rntn

messing around with rntn

add pos hmm

make sure hmm class init exists

nlp2 url

wiki data split by paragraph

update gitignore

better cost unsupervised2

ssl

add dropout files

fix class name

overfitting

bias in right order

add clustering extras + urls

supervised class

update comment

add visualization

airline example

add bonus message

remove irrelevant comments

minor changes

numpy class

add nonlinearity to tf cnn

tf load and save

remove unnecessary code

small update to logistic code

bayesian ab testing

tiny fix

update url

fix

add word2idx json

add batch + tf rnn

update tensorflow dropout

update bias correctly

comment out the right derivative

bayesian examples

k-means additions

add url

add regularization code and overfitting code for linear and logistic

supervised class regression

init for unsupervised class

init for ann2

ensembles class

tiny fix

alt nesterov

add dlc url ab testing

add dlc url to ann

add dlc url to ann2

update cnn urls

update hmm urls

update linear urls

update logistic urls

update nlp urls

update urls nlp2

update urls numpy

update urls rnn

update urls supervised

update urls supervised2

update urls unsupervised

update urls unsupervised2

add course url to readme

cleanup

change it back

remove old hmm

change it back

add tic tac toe to rl

add policy iteration and value iteration

fix dt

update readme with course links

update

update comment

tiny update

more correct

add url

add course url

update

remove old line

add course urls and rl files

add dlc url

re-add linear programming linear regression

update

cnn typo

fix relu derivative

cast y to int

misc updates

update tf

update for tf1.0

tiny xor update

update xor label

change var names

initial commit rl2

update readme

update rl2

make tf example compatible with python 3

tiny update

add done flag

use list copy

make purity work

different optimizer

use brown corpus instead of wiki

add brown corpus

update glove

"a" only gets updated at top of loop in q-learning

updating a to a2 is ineffectual as it gets overwritten at top of loop before use anyway. q-learning is off policy so that line is misleading

tiny fix

plot mean images

add new examples

one more

add brown

update

add sklearn example

update

add brown

oops

oops

Set theme jekyll-theme-cayman and migrate Page Generator content

Update index.md

test

tf scan

hmm tf

tf language model

actual tf language model

updates

different way of catching loops

minor change

extra help finding files

help finding files cnn

oops

add extra reading ann 2

Updating cnn_theano code according to theano API last update.

update

update

add extra reading rl

extra reading rl2

add hints for data

oops

update name

clean up

more reading

add relu

update

add new theano

fix

tf unsupervised deep learning

update custom cnn

test single autoencoder

theano autoencoder

tiny update

tiny update

softplus

py 3

test other means

change it back

better rmsprop

make it like tensorflow

more materials

python3 compatibility for web service example

add updated stuff that had not been pushed

reading material

fix ucb1

change back numbers

fix

fix s,a,r tuples

Fix conv

gans

add link

more links

minor updates

small changes

tiny fix

misc updates

new examples

update kmeans mnist

just for fun

oops

Author: Lazy Programmer

.gitignore

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+*.DS_Store
+*.pyc
+large_files
+large_files/*
+nlp_class2/chunking/*

README.md

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+machine_learning_examples
+=========================
+
+A collection of machine learning examples and tutorials.
+
+Find associated tutorials at https://lazyprogrammer.me
+
+Find associated courses at https://deeplearningcourses.com
+
+
+Direct Course Links
+===================
+
+Deep Learning Prerequisites: The Numpy Stack in Python
+https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python
+
+Deep Learning Prerequisites: Linear Regression in Python
+https://deeplearningcourses.com/c/data-science-linear-regression-in-python
+
+Deep Learning Prerequisites: Logistic Regression in Python
+https://deeplearningcourses.com/c/data-science-logistic-regression-in-python
+
+Deep Learning in Python
+https://deeplearningcourses.com/c/data-science-deep-learning-in-python
+
+Cluster Analysis and Unsupervised Machine Learning in Python
+https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python
+
+Data Science: Supervised Machine Learning in Python
+https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python
+
+Bayesian Machine Learning in Python: A/B Testing
+https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
+
+Easy Natural Language Processing in Python
+https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python
+
+Practical Deep Learning in Theano and TensorFlow
+https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow
+
+Ensemble Machine Learning in Python: Random Forest and AdaBoost
+https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost
+
+Deep Learning: Convolutional Neural Networks in Python
+https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow
+
+Unsupervised Deep Learning in Python
+https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python
+
+Unsupervised Machine Learning: Hidden Markov Models in Python
+https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python
+
+Deep Learning: Recurrent Neural Networks in Python
+https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python
+
+Advanced Natural Language Processing: Deep Learning in Python
+https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python
+
+Artificial Intelligence: Reinforcement Learning in Python
+https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python
+
+Advanced AI: Deep Reinforcement Learning in Python
+https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python
+
+Deep Learning: GANs and Variational Autoencoders
+https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders
+

ab_testing/bayesian_bandit.py

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+# From the course: Bayesin Machine Learning in Python: A/B Testing
+# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
+# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.stats import beta
+
+
+NUM_TRIALS = 2000
+BANDIT_PROBABILITIES = [0.2, 0.5, 0.75]
+
+
+class Bandit(object):
+  def __init__(self, p):
+    self.p = p
+    self.a = 1
+    self.b = 1
+
+  def pull(self):
+    return np.random.random() < self.p
+
+  def sample(self):
+    return np.random.beta(self.a, self.b)
+
+  def update(self, x):
+    self.a += x
+    self.b += 1 - x
+
+
+def plot(bandits, trial):
+  x = np.linspace(0, 1, 200)
+  for b in bandits:
+    y = beta.pdf(x, b.a, b.b)
+    plt.plot(x, y, label="real p: %.4f" % b.p)
+  plt.title("Bandit distributions after %s trials" % trial)
+  plt.legend()
+  plt.show()
+
+
+def experiment():
+  bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]
+
+  sample_points = [5,10,20,50,100,200,500,1000,1500,1999]
+  for i in xrange(NUM_TRIALS):
+
+    # take a sample from each bandit
+    bestb = None
+    maxsample = -1
+    allsamples = [] # let's collect these just to print for debugging
+    for b in bandits:
+      sample = b.sample()
+      allsamples.append("%.4f" % sample)
+      if sample > maxsample:
+        maxsample = sample
+        bestb = b
+    if i in sample_points:
+      print "current samples: %s" % allsamples
+      plot(bandits, i)
+
+    # pull the arm for the bandit with the largest sample
+    x = bestb.pull()
+
+    # update the distribution for the bandit whose arm we just pulled
+    bestb.update(x)
+
+
+if __name__ == "__main__":
+  experiment()

ab_testing/chisquare.py

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+# From the course: Bayesin Machine Learning in Python: A/B Testing
+# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
+# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import chi2, chi2_contingency
+
+# contingency table
+#        click       no click
+#------------------------------
+# ad A |   a            b
+# ad B |   c            d
+#
+# chi^2 = (ad - bc)^2 (a + b + c + d) / [ (a + b)(c + d)(a + c)(b + d)]
+# degrees of freedom = (#cols - 1) x (#rows - 1) = (2 - 1)(2 - 1) = 1
+
+# short example
+
+# T = np.array([[36, 14], [30, 25]])
+# c2 = np.linalg.det(T)**2 * T.sum() / ( T[0].sum()*T[1].sum()*T[:,0].sum()*T[:,1].sum() )
+# p_value = 1 - chi2.cdf(x=c2, df=1)
+
+# equivalent:
+# (36-31.429)**2/31.429+(14-18.571)**2/18.571 + (30-34.571)**2/34.571 + (25-20.429)**2/20.429
+
+
+class DataGenerator:
+  def __init__(self, p1, p2):
+    self.p1 = p1
+    self.p2 = p2
+
+  def next(self):
+    click1 = 1 if (np.random.random() < self.p1) else 0
+    click2 = 1 if (np.random.random() < self.p2) else 0
+    return click1, click2
+
+
+def get_p_value(T):
+  # same as scipy.stats.chi2_contingency(T, correction=False)
+  det = T[0,0]*T[1,1] - T[0,1]*T[1,0]
+  c2 = float(det) / T[0].sum() * det / T[1].sum() * T.sum() / T[:,0].sum() / T[:,1].sum()
+  p = 1 - chi2.cdf(x=c2, df=1)
+  return p
+
+
+def run_experiment(p1, p2, N):
+  data = DataGenerator(p1, p2)
+  p_values = np.empty(N)
+  T = np.zeros((2, 2)).astype(np.float32)
+  for i in xrange(N):
+    c1, c2 = data.next()
+    T[0,c1] += 1
+    T[1,c2] += 1
+    # ignore the first 10 values
+    if i < 10:
+      p_values[i] = None
+    else:
+      p_values[i] = get_p_value(T)
+  plt.plot(p_values)
+  plt.plot(np.ones(N)*0.05)
+  plt.show()
+
+run_experiment(0.1, 0.11, 20000)

ab_testing/ci_comparison.py

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+# From the course: Bayesin Machine Learning in Python: A/B Testing
+# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
+# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.stats import beta, norm
+
+T = 501 # number of coin tosses
+true_ctr = 0.5
+a, b = 1, 1 # beta priors
+plot_indices = (10, 20, 30, 50, 100, 200, 500)
+data = np.empty(T)
+for i in xrange(T):
+  x = 1 if np.random.random() < true_ctr else 0
+  data[i] = x
+
+  # update a and b
+  a += x
+  b += 1 - x
+
+  if i in plot_indices:
+    # maximum likelihood estimate of ctr
+    p = data[:i].mean()
+    n = i + 1 # number of samples collected so far
+    std = np.sqrt(p*(1-p)/n)
+    
+    # gaussian
+    x = np.linspace(0, 1, 200)
+    g = norm.pdf(x, loc=p, scale=std)
+    plt.plot(x, g, label='Gaussian Approximation')
+
+    # beta
+    posterior = beta.pdf(x, a=a, b=b)
+    plt.plot(x, posterior, label='Beta Posterior')
+    plt.legend()
+    plt.title("N = %s" % n)
+    plt.show()

ab_testing/convergence.py

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+# From the course: Bayesin Machine Learning in Python: A/B Testing
+# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
+# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
+import matplotlib.pyplot as plt
+import numpy as np
+from bayesian_bandit import Bandit
+
+
+def run_experiment(p1, p2, p3, N):
+  bandits = [Bandit(p1), Bandit(p2), Bandit(p3)]
+
+  data = np.empty(N)
+  
+  for i in xrange(N):
+    # thompson sampling
+    j = np.argmax([b.sample() for b in bandits])
+    x = bandits[j].pull()
+    bandits[j].update(x)
+
+    # for the plot
+    data[i] = x
+  cumulative_average_ctr = np.cumsum(data) / (np.arange(N) + 1)
+
+  # plot moving average ctr
+  plt.plot(cumulative_average_ctr)
+  plt.plot(np.ones(N)*p1)
+  plt.plot(np.ones(N)*p2)
+  plt.plot(np.ones(N)*p3)
+  plt.ylim((0,1))
+  plt.xscale('log')
+  plt.show()
+
+
+run_experiment(0.2, 0.25, 0.3, 100000)

ab_testing/demo.py

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+# From the course: Bayesin Machine Learning in Python: A/B Testing
+# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
+# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import beta
+
+def plot(a, b, trial, ctr):
+  x = np.linspace(0, 1, 200)
+  y = beta.pdf(x, a, b)
+  mean = float(a) / (a + b)
+  plt.plot(x, y)
+  plt.title("Distributions after %s trials, true rate = %.1f, mean = %.2f" % (trial, ctr, mean))
+  plt.show()
+
+true_ctr = 0.3
+a, b = 1, 1 # beta parameters
+show = [0, 5, 10, 25, 50, 100, 200, 300, 500, 700, 1000, 1500]
+for t in xrange(1501):
+  coin_toss_result = (np.random.random() < true_ctr)
+  if coin_toss_result:
+    a += 1
+  else:
+    b += 1
+
+  if t in show:
+    plot(a, b, t+1, true_ctr)

ab_testing/ttest.py

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+# From the course: Bayesin Machine Learning in Python: A/B Testing
+# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
+# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
+import numpy as np
+from scipy import stats
+
+# generate data
+N = 10
+a = np.random.randn(N) + 2 # mean 2, variance 1
+b = np.random.randn(N) # mean 0, variance 1
+
+# roll your own t-test:
+var_a = a.var(ddof=1) # unbiased estimator, divide by N-1 instead of N
+var_b = b.var(ddof=1)
+s = np.sqrt( (var_a + var_b) / 2 ) # balanced standard deviation
+t = (a.mean() - b.mean()) / (s * np.sqrt(2.0/N)) # t-statistic
+df = 2*N - 2 # degrees of freedom
+p = 1 - stats.t.cdf(np.abs(t), df=df) # one-sided test p-value
+print "t:\t", t, "p:\t", 2*p # two-sided test p-value
+
+# built-in t-test:
+t2, p2 = stats.ttest_ind(a, b)
+print "t2:\t", t2, "p2:\t", p2