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Annotation 7545566399756

#ML-engineering #ML_in_Action #learning #machine #software-engineering
ML engineers need to know just enough software development skills to be able to write modular code and implement unit tests. They don’t need to know about the intricacies of non-blocking asynchronous messaging brokering. They need just enough data engineering skills to build (and schedule the ETL for) feature datasets for their models, but not to construct a petabyte-scale streaming ingestion framework. They need just enough visualization skills to create plots and charts that communicate clearly what their research and models are doing, but not to develop dynamic web apps that have complex user experience (UX) components. They also need just enough project management experience to know how to properly define, scope, and control a project to solve a problem, but they need not go through a Project Management Professional (PMP) certification
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Annotation 7548869938444

#ML-engineering #ML_in_Action #learning #machine #software-engineering
Adding to the complexity of ML projects are two other critical elements that are not shared by (most) traditional software development projects: a frequent lack of detail in project expectations and the relative industry immaturity in tooling. Both aspects are no different from the state of software engineering in the early 1990s. Businesses then were unsure of how to best leverage new aspects of technological capability, tooling was woefully underdeveloped, and many projects failed to meet the expectations of those who were commissioning engineering teams to build them. ML work is (from my biased view of working with only so many companies) at the same place now in the second decade of the 21st century that software engineering was 30 years ago.
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Flashcard 7560722255116

Tags
#has-images #recurrent-neural-networks #rnn
[unknown IMAGE 7101515435276]
Question
A schematic high-level representation of the proposed model architecture is shown in Fig. 2. The structure of the model begins with its [...] for (i) the input variable (i.e., transaction counts) and (ii) optional covariates (time-invariant or time-varying inputs). These variable inputs enter the model through dedicated input layers at the top of the model’s architecture and are combined by simply concatenating them into a single long vector. This input signal then propagates through a series of intermediate layers including a specialized LSTM, or Long Short-Term Memory RNN neural network component.
Answer
input layers

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A schematic high-level representation of the proposed model architecture is shown in Fig. 2. The structure of the model begins with its input layers for (i) the input variable (i.e., transaction counts) and (ii) optional covariates (time-invariant or time-varying inputs). These variable inputs enter the model through dedicated input

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Annotation 7560725662988

#ML-engineering #ML_in_Action #learning #machine #software-engineering
ML engineers need to know just enough software development skills to be able to write modular code and implement unit tests. They don’t need to know about the intricacies of non-blocking asynchronous messaging brokering.
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ML engineers need to know just enough software development skills to be able to write modular code and implement unit tests. They don’t need to know about the intricacies of non-blocking asynchronous messaging brokering. They need just enough data engineering skills to build (and schedule the ETL for) feature datasets for their models, but not to construct a petabyte-scale streaming ingestion framework.

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Flashcard 7560727497996

Tags
#DAG #causal #edx
Question
Of course, in many cases we don't have enough expert knowledge to draw the [...] causal DAG that represents a causal structure of treatment A, outcome Y, and potential confounder L. In those cases we may propose several possible causal DAGs without being able choose a particular one. And that's fine, because those causal DAGs that we propose allow us to identify inconsistencies between our beliefs and our actions.
Answer
true

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Of course, in many cases we don't have enough expert knowledge to draw the true causal DAG that represents a causal structure of treatment A, outcome Y, and potential confounder L. In those cases we may propose several possible causal DAGs without being able choose

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Flashcard 7560729857292

Tags
#recurrent-neural-networks #rnn
Question
In this paper, we offer marketing analysts an alternative to these models by developing a deep learning based approach that does not rely on any ex-ante data labelling or feature engineering, but instead automatically detects behavioral [...] like seasonality or changes in inter-event timing patterns by learning directly from the prior transaction history
Answer
dynamics

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an alternative to these models by developing a deep learning based approach that does not rely on any ex-ante data labelling or feature engineering, but instead automatically detects behavioral <span>dynamics like seasonality or changes in inter-event timing patterns by learning directly from the prior transaction history <span>

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Annotation 7560731692300

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Recognizing relevant patterns in long input streams (> 100 actions) can turn out to be difficult for the human mind. To achieve better explainability, in many e-commerce applications consumer behavior can be viewed on the level of sessions
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Recognizing relevant patterns in long input streams (> 100 actions) can turn out to be difficult for the human mind. To achieve better explainability, in many e-commerce applications consumer behavior can be viewed on the level of sessions. A session is a well-defined visit of a consumer to a web- shop: a subsequence of events within the consumer’s history that lay no further apart than a predefined time difference. Here,

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Flashcard 7560734575884

Tags
#data-science #infrastructure
Question
[...] complexity is a huge problem for real-world data science because we have to deal with such a high level of inherent complexity that distinguishing between real problems and imaginary problems becomes hard
Answer
Incidental

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Incidental complexity is a huge problem for real-world data science because we have to deal with such a high level of inherent complexity that distinguishing between real problems and imaginary pr

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