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on 14-Sep-2023 (Thu)

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Flashcard 7590319885580

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

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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 predic- tion quality doesn’t matter (since the infrastructure can’t properly serve the predic- tions). If it’s overpowered, you’re effectively burning money on unused infrastructure and complexity
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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

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

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Annotation 7591147736332

#deep-learning #keras #lstm #python #sequence
You can normalize your dataset using the scikit-learn object MinMaxScaler
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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>

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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

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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

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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

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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

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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

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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>

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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
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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

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