Edited, memorised or added to reading queue

on 03-Nov-2025 (Mon)

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

Flashcard 7767067069708

Tags
#deep-learning #keras #lstm #python #sequence
Question
The Stacked LSTM is a model that has [...] hidden LSTM layers
Answer
multiple

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 Stacked LSTM is a model that has multiple hidden LSTM layers

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7767068380428

Tags
#tensorflow #tensorflow-certificate
Question

Bag of tricks to improve model

3. [...] the model - more epochs, more data examples

Answer
Fit

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
Bag of tricks to improve model 3. Fit the model - more epochs, more data examples

Original toplevel document

TfC_02_classification-PART_1
nse(10, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid') ]) model.compile(optimizer=tf.keras.optimizers.Adam(), loss=tf.keras.losses.binary_crossentropy, metrics=['accuracy']) <span>Bag of tricks to improve model Create model - more layers, more neurons, different activation Compile mode - other loss, other optimizer, change optimizer parameters Fit the model - more epochs, more data examples # plots model predictions agains true data import numpy as np def plot_decision_boundry(model, X, y): """ Take in a trained model, features and labels and create numpy.meshgrid of the d






Flashcard 7767070215436

Tags
#tensorflow #tensorflow-certificate
Question
In case of labels as integeres use [...]Crossentropy
Answer
SparseCategorical

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
In case of labels as integeres use SparseCategoricalCrossentropy

Original toplevel document

TfC_02_classification-PART_2
y-axis -> true label x-axis -> predicted label # Create confusion metrics from sklearn.metrics import confusion_matrix y_preds = model_8.predict(X_test) confusion_matrix(y_test, y_preds) <span>important: This time there is a problem with loss function. In case of categorical_crossentropy the labels have to be one-hot encoded In case of labels as integeres use SparseCategoricalCrossentropy # Get the patterns of a layer in our network weights, biases = model_35.layers[1].get_weights() <span>






Annotation 7767072836876

#ML-engineering #ML_in_Action #learning #machine #software-engineering
ML engineers also need just enough project management experience to know how to properly define, scope, and control a project to solve a problem, but they need not go through a Project Management Professional (PMP) certification
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
ation skills to create plots and charts that communicate clearly what their research and models are doing, but not to develop dynamic web apps that have complex user experience (UX) components. <span>They also need just enough project management experience to know how to properly define, scope, and control a project to solve a problem, but they need not go through a Project Management Professional (PMP) certification <span>

Original toplevel document (pdf)

cannot see any pdfs