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Annotation 7629774392588

#CURRENT_READING #deep #keras #learning #tensorflow #tfc-II

model amalgamation.

In this approach, models are broken into composable units that share and adapt components to achieve different objectives with the same initial data. The components are interconnected in a variety of connectivity patterns, in which each component learns communication interfaces between the models through design, without the necessity of a backend application
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Flashcard 7793493806348

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

3 common examples for managing state:

  • A long sequence was split into multiple subsequences (many samples each with many time steps). State should be reset after the network has been exposed to the [...] by making the LSTM stateful, turning off the shuffling of subsequences, and resetting the state after each epoch
Answer
entire sequence

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ommon examples for managing state: A long sequence was split into multiple subsequences (many samples each with many time steps). State should be reset after the network has been exposed to the <span>entire sequence by making the LSTM stateful, turning off the shuffling of subsequences, and resetting the state after each epoch <span>

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Flashcard 7793495903500

Tags
#deep #keras #learning #tensorflow #tfc-II
Question
Model development for production continues to be a combination of automatic and hand-designed learning—which is often crucial for proprietary needs or advantages. But designing by hand does not mean starting from scratch; typically, you would start with a [...] and make tweaks and adjustments.
Answer
stock model

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of automatic and hand-designed learning—which is often crucial for proprietary needs or advantages. But designing by hand does not mean starting from scratch; typically, you would start with a <span>stock model and make tweaks and adjustments. <span>

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Annotation 7793499049228

#deep-learning #keras #lstm #python #sequence
RNNs are inherently deep in time, since their hidden state is a function of all previous hidden states
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RNNs are inherently deep in time, since their hidden state is a function of all previous hidden states. The question that inspired this paper was whether RNNs could also benefit from depth in space; that is from stacking multiple recurrent hidden layers on top of each other, just as feed

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Flashcard 7793500884236

Tags
#Linux
Question

If you have a list of files, but you only want to delete files older the a certain date, for example, a maildir folder with 5 years worth of email, and you want to delete everything older then 2 years, then run the following command.

find . -type f -mtime +730 -maxdepth 1 [...] rm {} \;

Answer
-exec

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ain date, for example, a maildir folder with 5 years worth of email, and you want to delete everything older then 2 years, then run the following command. find . -type f -mtime +730 -maxdepth 1 <span>-exec rm {} \; <span>

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How to delete all files before a certain date in Linux
How to delete all files before a certain date in Linux Posted on: September 15, 2015 If you have a list of files, but you only want to delete files older the a certain date, for example, a maildir folder with 5 years worth of email, and you want to delete everything older then 2 years, then run the following command. find . -type f -mtime +XXX -maxdepth 1 -exec rm {} \; The syntax of this is as follows. find – the command that finds the files . – the dot signifies the current folder. You can change this to something like /home/someuser/mail/somedomain/






Annotation 7793503243532

#ML_in_Action #learning #machine #software-engineering
I’ve been a bad data scientist. I’ve been an even worse ML engineer. It took me years to learn why that is. That stubbornness and resistance to solving problems in the simplest way created a lot of headaches for others, both in the sheer number of cancelled projects while I was at companies and in the unmaintainable technical debt that I left in my wake
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each one of them why, I’ve gotten the same demoralizing answer that has made me regret my implementations: “No one can figure it out to make changes to it, and it’s too important to turn off.” <span>I’ve been a bad data scientist. I’ve been an even worse ML engineer. It took me years to learn why that is. That stubbornness and resistance to solving problems in the simplest way created a lot of headaches for others, both in the sheer number of cancelled projects while I was at companies and in the unmaintainable technical debt that I left in my wake. It wasn’t until my most recent job, working as a resident solutions architect at Data- bricks (essentially a vendor field consultant), that I started to learn where I had gone wrong an

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