Edited, memorised or added to reading queue

on 14-Sep-2023 (Thu)

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

Flashcard 7590319885580

Tags
#has-images #knative #serverless
[unknown IMAGE 7590320409868]
[unknown IMAGE 7590316477708]

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





Annotation 7591143542028

#ML-engineering #ML_in_Action #learning #machine #software-engineering
Perhaps the most confusing and complex part of ML project work for newer teams is in how to build a cost-effective deployment strategy. If it’s underpowered, the prediction quality doesn’t matter (since the infrastructure can’t properly serve the predictions). If it’s overpowered, you’re effectively burning money on unused infrastructure and complexity
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs



Flashcard 7591145901324

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
Consumer [...] are inherently sequential and of varying lengths T , making RNNs a natural model choice.
Answer
histories

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
Consumer histories are inherently sequential and of varying lengths T , making RNNs a natural model choice.

Original toplevel document (pdf)

cannot see any pdfs






Annotation 7591147736332

#deep-learning #keras #lstm #python #sequence
You can normalize your dataset using the scikit-learn object MinMaxScaler
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
e in the range of 0 and 1. You could check for these observations prior to making predictions and either remove them from the dataset or limit them to the pre-defined maximum or minimum values. <span>You can normalize your dataset using the scikit-learn object MinMaxScaler <span>

Original toplevel document (pdf)

cannot see any pdfs



Flashcard 7591149833484

Tags
#recurrent-neural-networks #rnn
Question
Contrary to subscription-based or contractual settings where customer ‘‘churn” events are directly observable, customer defection in [...] business settings is by definition unobserved by the firm and thus needs to be indirectly inferred from past transaction behavior (Reinartz & Kumar, 2000; Gupta et al., 2006).
Answer
non-contractual

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
Contrary to subscription-based or contractual settings where customer ‘‘churn” events are directly observable, customer defection in non-contractual business settings is by definition unobserved by the firm and thus needs to be indirectly inferred from past transaction behavior (Reinartz & Kumar, 2000; Gupta et al., 2006). </spa

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7591151668492

Tags
#recurrent-neural-networks #rnn
Question
[...] future customer behavior and making individual-level predictions for a firm’s customer base is crucial to any organization that wants to manage its customer portfolio proactively.
Answer
Anticipating

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
Anticipating future customer behavior and making individual-level predictions for a firm’s customer base is crucial to any organization that wants to manage its customer portfolio proactively. </spa

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7591154289932

Tags
#deep-learning #embeddings
Question
With the similar idea of how we get word embeddings, we can make an analogy like this: a word is like a product; a sentence is like a sequence of ONE customer’s shopping sequence; an article is like a sequence of ALL customers’ shopping sequence. This embedding technique allows us to represent product or user as low dimensional continuous vectors, while the one-hot encoding method will lead to the [...] for the machine learning models
Answer
curse of dimensionality

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
ce of ALL customers’ shopping sequence. This embedding technique allows us to represent product or user as low dimensional continuous vectors, while the one-hot encoding method will lead to the <span>curse of dimensionality for the machine learning models <span>

Original toplevel document (pdf)

cannot see any pdfs






Annotation 7591156911372

#ML_in_Action #learning #machine #software-engineering
ML engineering applies a system around this staggering level of complexity. It uses a set of standards, tools, processes, and methodology that aims to minimize the chances of abandoned, misguided, or irrelevant work being done in an effort to solve a business problem or need.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
ML engineering applies a system around this staggering level of complexity. It uses a set of standards, tools, processes, and methodology that aims to minimize the chances of abandoned, misguided, or irrelevant work being done in an effort to solve a business problem or need. It, in essence, is the road map to creating ML-based systems that can be not only deployed to production, but also maintained and updated for years in the future, allowing businesses t

Original toplevel document (pdf)

cannot see any pdfs