Edited, memorised or added to reading queue

on 02-Nov-2025 (Sun)

Do you want BuboFlash to help you learning these things? Click here to log in or create user.

Flashcard 7766918434060

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
To increase the match of the model's effective capacity and the complexity of the task at hand, the analyst needs to tune both the parameters and the hyperparameters of the model. Given how sensitive LSTM models are to [...] tuning, this area requires particular attention.
Answer
hyperparameter

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
the model's effective capacity and the complexity of the task at hand, the analyst needs to tune both the parameters and the hyperparameters of the model. Given how sensitive LSTM models are to <span>hyperparameter tuning, this area requires particular attention. <span>

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7766919744780

Tags
#deep-learning #keras #lstm #python #sequence
Question
Each epoch can be partitioned into groups of [...] pattern pairs called batches
Answer
input-output

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Each epoch can be partitioned into groups of input-output pattern pairs called batches

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7766921579788

Tags
#deep-learning #keras #lstm #python #sequence
Question
The additional [...] layers are understood to recombine the learned representation from prior layers and create new representations at high levels of abstraction.
Answer
hidden

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The additional hidden layers are understood to recombine the learned representation from prior layers and create new representations at high levels of abstraction.

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7766923414796

Question
lifelong deep neural networks ([...]),
Answer
L-DNN

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
lifelong deep neural networks (L-DNN),

Original toplevel document

Deep Learning Has Reinvented Quality Control in Manufacturing—but It Hasn’t Gone Far Enough AI systems that make use of “lifelong learning” techniques are more flexible and faster to train
These so-called continual or lifelong learning systems, and in particular lifelong deep neural networks (L-DNN), were inspired by brain neurophysiology. These deep learning algorithms separate feature training and rule training and are able to add new rule information on the fly. While they still