Edited, memorised or added to reading queue

on 28-Nov-2025 (Fri)

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

Flashcard 7773192064268

Tags
#has-images #tensorflow #tensorflow-certificate
[unknown IMAGE 7626420784396]
Question

Deep Learning mantras: ;)

Building model: experiment
[...] model: visualize

Answer
Evaluation

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
Deep Learning mantras: ;) Building model: experiment Evaluation model: visualize

Original toplevel document

TfC 01 regression
more epochs, more data ### How? # from smaller model to larger model Evaluating models Typical workflow: build a model -> fit it -> evaulate -> tweak -> fit > evaluate -> .... <span>Building model: experiment Evaluation model: visualize What can visualize? the data model itself the training of a model predictions ## The 3 sets (or actually 2 sets: training and test set) tf.random.set_seed(999) X_train, X_test = tf.spli






Flashcard 7773193899276

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question

The learning mechanism of the recurrent neural network thus involves:

...

(3) the [...] algorithm, that changes the parameters of the RNN based on the gradient.

Answer
optimization

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 learning mechanism of the recurrent neural network thus involves: ... (3) the optimization algorithm, that changes the parameters of the RNN based on the gradient.

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7773196520716

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
While an RNN can carry forward useful information from one timestep to the next, however, it is much less effective at capturing long-term dependencies (Bengio, Simard, & Frasconi, 1994; Pascanu, Mikolov, & Bengio, 2013). This limitation turns out to be a crucial problem in [...] analytics.
Answer
marketing

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
it is much less effective at capturing long-term dependencies (Bengio, Simard, & Frasconi, 1994; Pascanu, Mikolov, & Bengio, 2013). This limitation turns out to be a crucial problem in <span>marketing analytics. <span>

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7773197831436

Tags
#has-images #tensorflow #tensorflow-certificate
[unknown IMAGE 7626420784396]
Question
Huber
  • tf.keras.losses.Huber()
  • [...] of MSE and MAE less sensitive to outliers than MSE
Answer
combintion

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
Huber tf.keras.losses.Huber() combintion of MSE and MAE less sensitive to outliers than MSE

Original toplevel document

TfC 01 regression
st_labels, c="green", label="Testing data") plt.scatter(test_data, predictions, c="red", label="Predictions") plt.legend(); Common regression evaluation metrics keyboard_arrow_down Introduction <span>For regression problems: MAE tf.keras.losses.MAE() tf.metrics.mean_absolute_error() great starter metrics for any regression problem MSE tf.keras.losses.MSE() tf.metrics.mean_square_error() when larger errors are more significant that smaller errors Huber tf.keras.losses.Huber() combintion of MSE and MAE less sensitive to outliers than MSE Take away: You should minimize the time between your experiments (that's way you should start with smaller models). The more experiments you do, the more things you figure out that don't work. Tracking your experiments One really good habit is to track the results of your experiments. There are tools to help us! Resource: Try: Tensorboard - a component of Tensorflow library t