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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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Annotation 7629776751884

#deep #keras #learning #tensorflow #tfc-II
In addition, model amalgamation can be used to train Internet of Things (IoT) devices for data enrichment, turning IoT sensors from static to dynamically learning devices — a technique called model fusion
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Flashcard 7629871910156

Tags
#causality #has-images #statistics


Question
Consider the following general kind of path, where → · · · → denotes a directed path: 𝑇 → · · · → 𝑍 ← · · · ← 𝑌 . [...] on 𝑍 , or any descendant of 𝑍 in a path like this, will induce collider bias
Answer
Conditioning

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Consider the following general kind of path, where → · · · → denotes a directed path: 𝑇 → · · · → 𝑍 ← · · · ← 𝑌 . Conditioning on 𝑍 , or any descendant of 𝑍 in a path like this, will induce collider bias

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Annotation 7662792215820

#deep #keras #learning #tensorflow #tfc-II
advancements in design patterns for structured data evolved with the introduction of the wide-and-deep model pattern
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advancements in design patterns for structured data evolved with the introduction of the wide-and-deep model pattern, outlined in “Wide & Deep Learning for Recommender Systems” (https://arxiv.org/abs/1606.07792) by Heng- Tze Cheng et. al, at the technology-agnostic research group Google Research,

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Annotation 7662796147980

#tensorflow #tensorflow-certificate
Normalization 
# Start from scratch
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf ## Borrow a few classes from sci-kit learn
from sklearn.compose import make_column_transformer
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
from sklearn.model_selection import train_test_split #Create column transformer
ct = make_column_transformer((MinMaxScaler(), ['age', 'bmi', 'children']), # turn all values in these columns between 0 and 1 (OneHotEncoder(handle_unknown='ignore', dtype="int32"), ['sex', 'smoker', 'region'])) # Create X and y
X = insurance.drop('charges', axis=1)
y = insurance['charges'] # Split datasets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Fit the column transformer on training data and apply to both datasets (train and test)
ct.fit(X_train) # Transform data
X_train_normalize = ct.transform(X_train)
X_test_normalize = ct.transform(X_test)
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TfC_01_FINAL_EXAMPLE.ipynb
he right shape Scale features (normalize or standardize) Neural networks tend to prefer normalization. Normalization - adjusting values measured on different scales to a notionally common scale <span>Normalization # Start from scratch import pandas as pd import matplotlib.pyplot as plt import tensorflow as tf ## Borrow a few classes from sci-kit learn from sklearn.compose import make_column_transformer from sklearn.preprocessing import MinMaxScaler, OneHotEncoder from sklearn.model_selection import train_test_split #Create column transformer ct = make_column_transformer((MinMaxScaler(), ['age', 'bmi', 'children']), # turn all values in these columns between 0 and 1 (OneHotEncoder(handle_unknown='ignore', dtype="int32"), ['sex', 'smoker', 'region'])) # Create X and y X = insurance.drop('charges', axis=1) y = insurance['charges'] # Split datasets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Fit the column transformer on training data and apply to both datasets (train and test) ct.fit(X_train) # Transform data X_train_normalize = ct.transform(X_train) X_test_normalize = ct.transform(X_test) <span>



Flashcard 7662797720844

Tags
#tensorflow #tensorflow-certificate
Question
# Tensors can be indexed just like Python lists.

# Get the first 2 elements of each [...]
A[:2, :2, :2, :2]

Answer
dimension

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Tensors indexing
# Tensors can be indexed just like Python lists. # Get the first 2 elements of each dimension A[:2, :2, :2, :2]






Flashcard 7662799555852

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 [...] -maxdepth 1 -exec rm {} \;

Answer
+730

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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 <span>+730 -maxdepth 1 -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/






Flashcard 7662801390860

Tags
#deep-learning #keras #lstm #python #sequence
Question
The LSTM expects input data to have the dimensions: samples, time steps, and [...].
Answer
features

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The LSTM expects input data to have the dimensions: samples, time steps, and features.

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