fa-ahmad

This returns the names and data types of all columns of the primary key for the tablename table:

FROM   pg_index i
JOIN   pg_attribute a ON a.attrelid = i.indrelid
                     AND a.attnum = ANY(i.indkey)
WHERE  i.indrelid = 'tablename'::regclass
AND    i.indisprimary;

fa-ahmad

The indirection operator captures the output of any SQL query into a Python variable. In the example below the variable output will contain the result of the query. The query used here is irrelevant and only used for illustrating the usage.

%%sql output << 
SELECT a.attname, format_type(a.atttypid, a.atttypmod) AS data_type, a.attnotnull, i.indisprimary
FROM   pg_index i
JOIN   pg_attribute a ON a.attrelid = i.indrelid
 AND a.attnum = ANY(i.indkey)

fa-ahmad

Use the entr command to watch the Notebook for changes.

ls myfile.ipynb| entr -r jupyter nbconvert myfile.ipynb --to slides --post serve --ServePostProcessor.open_in_browser=False

The URL of the slides gets printed in the terminal:

Serving your slides at http://127.0.0.1:8000/myfile.slides.html

Sources:

fa-ahmad

class MyModel(Model):
  def __init__(self):
    super(MyModel, self).__init__()
    self.conv1 = Conv2D(32, 3, activation='relu')
    self.flatten = Flatten()
    self.d1 = Dense(128, activation='relu')
    self.d2 = Dense(10)

  def call(self, x):
    x = self.conv1(x)

fa-ahmad

from pomegranate import *
d1 = DiscreteDistribution({'A' : 0.35, 'C' : 0.20, 'G' : 0.05, 'T' : 0.40})
d2 = DiscreteDistribution({'A' : 0.25, 'C' : 0.25, 'G' : 0.25, 'T' : 0.25})
d3 = DiscreteDistribution({'A' : 0.10, 'C' : 0.40, 'G' : 0.40, 'T' : 0.10})

s1 = State(d1, name="s1")
s2 = State(d2, name="s2")
s3 = State(d3, name="s3")

model = HiddenMarkovModel('example')

fa-ahmad

import torch
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 3x3 square convolution

fa-ahmad

TypeError                                 Traceback (most recent call last)
<ipython-input-44-a553646a889c> in <module>
    22     for i, samples in enumerate([samples_1, samples_10, samples_100]):
    23         results[clf_name][i] = \
---> 24         train_predict(clf, samples, X_train, y_train, X_test, y_test)
    25
    26 # Run metrics visualization for the three supervised learning models chosen
 
<ipython-input-36-2fd67492e721> in train_predict(learner, sample_size, X_train, y_train, X_test, y_test)
    19     # TODO: Fit the learner to the training data using slicing with ‘sample_size’ using .fit(training_features[:], training_labels[:])