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Annotation 7096404151564

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
To summarize, our contributions are the following: (i) we show how consumer behavior can be predicted without sophisticated feature engineering by using RNNs; (ii) we provide an empirical comparison of prediction performance on real-world e-commerce data; and (iii) we demonstrate how RNNs are helpful in explaining the predictions for individual consumers
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Annotation 7625274428684

#deep-learning #keras #lstm #python #sequence

We can summarize the 3 key benefits of LSTMs as:

2. Possesses memory to overcome the issues of long-term temporal dependency with input sequences
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We can summarize the 3 key benefits of LSTMs as: 1. Overcomes the technical problems of training an RNN, namely vanishing and exploding gradients. 2. Possesses memory to overcome the issues of long-term temporal dependency with input sequences 3. Process input sequences and output sequences time step by time step, allowing variable length inputs and outputs

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Flashcard 7625277312268

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
For event-stream RNNs, history inputs x t ∈ R 20 consist of a one-hot encoding of the [...] and the time difference. For session-stream RNNs, history inputs s t ∈ R 23 represent sessions with binary indicators which action types occurred, the time difference to the previous session and the characteristics described in Sec. 3.2. Time differences and, in case of session-stream RNNs, the total session event counts are logarithmized.
Answer
action type

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For event-stream RNNs, history inputs x t ∈ R 20 consist of a one-hot encoding of the action type and the time difference. For session-stream RNNs, history inputs s t ∈ R 23 represent sessions with binary indicators which action types occurred, the time difference to the previous se

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Annotation 7625280457996

[unknown IMAGE 7104054824204] #deep-learning #has-images #keras #lstm #python #sequence
For example, if we had two time steps and one feature for a univariate sequence with two lag observations per row, it would be specified as on listing 4.5
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You can specify the input shape argument that expects a tuple containing the number of time steps and the number of features. For example, if we had two time steps and one feature for a univariate sequence with two lag observations per row, it would be specified as on listing 4.5

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Flashcard 7625282030860

Tags
#has-images #recurrent-neural-networks #rnn
[unknown IMAGE 7101511240972]
Question
To forecast future customer behavior, our model is trained using individual sequences of past transaction events, i.e., chronological accounts of a customer’s lifetime. The example in Table 2 describes one such customer’s transaction history over seven consecutive [...] time periods
Answer
discrete

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dual sequences of past transaction events, i.e., chronological accounts of a customer’s lifetime. The example in Table 2 describes one such customer’s transaction history over seven consecutive <span>discrete time periods <span>

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Flashcard 7625283865868

Tags
#deep-learning #keras #lstm #python #sequence
Question
A [...] single hidden layer Multilayer Perceptron can be used to approximate most functions. Increasing the depth of the network provides an alternate solution that requires fewer neurons and trains faster. Ultimately, adding depth it is a type of representational optimization.
Answer
sufficiently large

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A sufficiently large single hidden layer Multilayer Perceptron can be used to approximate most functions. Increasing the depth of the network provides an alternate solution that requires fewer neurons and t

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Annotation 7625285700876

To create an environment with a specific version of Python and multiple packages including a package with a specific version:

$ conda create -n <env_name> python=<version#> <packagename> <packagename> <packagename>=<version#>
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ays create a new environment for each project Install all the packages that you need in the new environment at the same time. Installing packages one at a time can lead to dependency conflicts. <span>To create an environment with a specific version of Python and multiple packages including a package with a specific version: $ conda create -n <env_name> python=<version#> <packagename> <packagename> <packagename>=<version#> Alternatively, you can use conda to install all the packages in a requirements.txt file. You can save a requirements.txt file from an existing environment, or manually create a new requ

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How to Manage Python Dependencies with Conda - ActiveState
rmine the Current Environment with Conda The current or active environment is shown in parentheses () or brackets [] at the beginning of the Anaconda Prompt or terminal: (<current_env>) $ <span>Recommendations for Avoiding Dependency Conflicts with Conda There are two simple rules to follow: Always create a new environment for each project Install all the packages that you need in the new environment at the same time. Installing packages one at a time can lead to dependency conflicts. To create an environment with a specific version of Python and multiple packages including a package with a specific version: $ conda create -n <env_name> python=<version#> <packagename> <packagename> <packagename>=<version#> Alternatively, you can use conda to install all the packages in a requirements.txt file. You can save a requirements.txt file from an existing environment, or manually create a new requirements.txt for a different environment. To create a conda requirements.txt file from an existing environment: Activate your project environment. See section above entitled “How to Activate an Environment with Conda” for detai



Flashcard 7625287798028

Tags
#ML-engineering #ML_in_Action #learning #machine #software-engineering
Question
ML engineers need to know just enough software development skills to be able to write modular code and implement [...]. They don’t need to know about the intricacies of non-blocking asynchronous messaging brokering.
Answer
unit tests

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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.

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Flashcard 7625289895180

Tags
#English #vocabulary
Question

[...]

adjective

not likely to upset or harm anyone

Answer
innocuous

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innocuous adjective UK /ɪˈnɒkjuəs/ US not likely to upset or harm anyone






Annotation 7625292516620

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
To summarize, our contributions are the following: (i) we show how consumer behavior can be predicted without sophisticated feature engineering by using RNNs;
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To summarize, our contributions are the following: (i) we show how consumer behavior can be predicted without sophisticated feature engineering by using RNNs; (ii) we provide an empirical comparison of prediction performance on real-world e-commerce data; and (iii) we demonstrate how RNNs are helpful in explaining the predictions for individu

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