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on 01-Jul-2024 (Mon)

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Flashcard 7629499403532

Tags
#deep-learning #keras #lstm #python #sequence
Question
The [...], or LSTM, network is a type of Recurrent Neural Network.
Answer
Long Short-Term Memory

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The Long Short-Term Memory, or LSTM, network is a type of Recurrent Neural Network.

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Flashcard 7641054973196

Tags
#deep-learning #keras #lstm #python #sequence
Question
The caution is that LSTMs are not a [...] and to carefully consider the framing of your problem.
Answer
silver bullet

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The caution is that LSTMs are not a silver bullet and to carefully consider the framing of your problem.

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Flashcard 7642459016460

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
Consumer behavior is inherently sequential which makes [...]s a perfect fit.
Answer
RNN

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Consumer behavior is inherently sequential which makes RNNs a perfect fit.

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Flashcard 7642484968716

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
Feature engineering has been used broadly to refer to multiple aspects of feature [...], extraction, and transformation
Answer
creation

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Feature engineering has been used broadly to refer to multiple aspects of feature creation, extraction, and transformation

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Flashcard 7642488114444

Question

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> [...]=<version#> <packagename> <packagename> <packagename>=<version#>
Answer
python

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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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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 7642887621900

Tags
#ML-engineering #ML_in_Action #learning #machine #software-engineering
Question
In the experimentation phase, the largest causes of project failure are either the experimentation taking too long (testing [...] or spending too long fine- tuning an approach)
Answer
too many things

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In the experimentation phase, the largest causes of project failure are either the experimentation taking too long (testing too many things or spending too long fine- tuning an approach)

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Annotation 7642889456908

#deep-learning #keras #lstm #python #sequence
For example, red is 1, green is 2, and blue is 3. This is called label encoding or an integer encoding and is easily reversible
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Integer Encoding As a first step, each unique category value is assigned an integer value. For example, red is 1, green is 2, and blue is 3. This is called label encoding or an integer encoding and is easily reversible. For some variables, this may be enough. The integer values have a natural ordered relationship between each other and machine learning algorithms may be able to understand and harness

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Annotation 7642891291916

#ML-engineering #ML_in_Action #learning #machine #software-engineering
By focusing on what will be built and why it needs to be built, both the DS team and the business are able to guide the discussion more fruitfully. Eschewing a conversation focused on how it will be built keeps the DS members of the group focused on the problem. Ignoring when it will be built by helps the business keep its focus aligned on the needs of the project. Avoiding discussing implementation details at this (very early, initial) stage of the project is not merely critical for the team to focus on the problem. Keeping the esoteric details of algorithms and solution design out of discussions with the larger team keeps the business unit members engaged.
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By focusing on what will be built and why it needs to be built, both the DS team and the business are able to guide the discussion more fruitfully. Eschewing a conversation focused on how it will be built keeps the DS members of the group focused on the problem. Ignoring when it will be built by helps the business keep its focus aligned on the needs of the project. Avoiding discussing implementation details at this (very early, initial) stage of the project is not merely critical for the team to focus on the problem. Keeping the esoteric details of algorithms and solution design out of discussions with the larger team keeps the business unit members engaged. After all, they really don’t care how many eggs go into the mix, what color the eggs are, or even what species laid the eggs; they just want to eat the cake when it’s done. We will cove

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Annotation 7642892864780

#ML-engineering #ML_in_Action #learning #machine #software-engineering
business unit members engaged. After all, they really don’t care how many eggs go into the mix, what color the eggs are, or even what species laid the eggs; they just want to eat the cake when it’s done.
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tage of the project is not merely critical for the team to focus on the problem. Keeping the esoteric details of algorithms and solution design out of discussions with the larger team keeps the <span>business unit members engaged. After all, they really don’t care how many eggs go into the mix, what color the eggs are, or even what species laid the eggs; they just want to eat the cake when it’s done. We will cover the processes of planning, having project expectation discussions with internal business customers, and general communications about ML work with a nontechnical audience a

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