Edited, memorised or added to reading queue

on 04-May-2024 (Sat)

Do you want BuboFlash to help you learning these things? Click here to log in or create user.

Annotation 7103910120716

#feature-engineering #lstm #recurrent-neural-networks #rnn
The RNN processes the entire sequence of available data without having to summarize it into features. Since customer transactions occur sequentially, they can be modeled as a sequence prediction task using an RNN as well, where all firm actions and customer responses are represented by elements in a vector. For instance, suppose a firm solicits customers either through phone, mail, or email (three channels), and customers may purchase across 17 product categories. All the analyst has to do is to encode each observation period (e.g., a day, a week, a month) as a vector of size 20, where all the values are equal to 0, except when a solicitation is sent, or a purchase is observed. If purchase seasonality is significant, e.g., if peaks in sales occur around Christmas, the analyst can also encode the current month using a one-hot vector of size 12, for a total vector length of 32 raw inputs.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs



Flashcard 7624075382028

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import [...]
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential(Dense(1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])

Answer
Sequential

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,1






Flashcard 7625208368396

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
As [...] are required directly in many practical applications, we use NLL also for evaluation. In some applications, the resulting ranking of consumers is more important than the probabilities themselves. For this reason, we also report the area under the ROC curve (AUC)
Answer
probability estimates

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
As probability estimates are required directly in many practical applications, we use NLL also for evaluation. In some applications, the resulting ranking of consumers is more important than the probabilities t

Original toplevel document (pdf)

cannot see any pdfs






Annotation 7626121940236

#feature-engineering #lstm #recurrent-neural-networks #rnn
using an RNN as well, where all firm actions and customer responses are represented by elements in a vector. For instance, suppose a firm solicits customers either through phone, mail, or email (three channels), and customers may purchase across 17 product categories. All the analyst has to do is to encode each observation period (e.g., a day, a week, a month) as a vector of size 20, where all the values are equal to 0, except when a solicitation is sent, or a purchase is observed. If purchase seasonality is significant, e.g., if peaks in sales occur around Christmas, the analyst can also encode the current month using a one-hot vector of size 12, for a total vector length of 32 raw inputs.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
N processes the entire sequence of available data without having to summarize it into features. Since customer transactions occur sequentially, they can be modeled as a sequence prediction task <span>using an RNN as well, where all firm actions and customer responses are represented by elements in a vector. For instance, suppose a firm solicits customers either through phone, mail, or email (three channels), and customers may purchase across 17 product categories. All the analyst has to do is to encode each observation period (e.g., a day, a week, a month) as a vector of size 20, where all the values are equal to 0, except when a solicitation is sent, or a purchase is observed. If purchase seasonality is significant, e.g., if peaks in sales occur around Christmas, the analyst can also encode the current month using a one-hot vector of size 12, for a total vector length of 32 raw inputs. <span>

Original toplevel document (pdf)

cannot see any pdfs



Annotation 7626124037388

#deep-learning #keras #lstm #python #sequence
if you are using a stateful LSTM, you must reset state after evaluating the network on a validation dataset or after making predictions
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
The internal state in LSTM layers is also accumulated when evaluating a network and when making predictions. Therefore, if you are using a stateful LSTM, you must reset state after evaluating the network on a validation dataset or after making predictions

Original toplevel document (pdf)

cannot see any pdfs