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1
Practical Weak Supervision: Doing More with Less Data
O'Reilly Media
Tok
,
Wee Hyong
,
Bahree
,
Amit
,
Filipi
,
Senja
dataset
labeling
functions
snorkel
labels
supervision
function
spark
import
validation
outdoor
models
indoor
classification
fake
classifiers
figure
accuracy
augmentation
datasets
vision
databricks
labeled
scale
dataframe
azure
programming
cluster
abstain
liar
labelmodel
transfer
output
fakenewsnet
transformers
statement
versus
pretrained
resnet
epoch
shown
detection
row
tags
tuning
python
rate
predictions
hugging
lfs
Godina:
2021
Jezik:
english
Fajl:
EPUB, 6.92 MB
Vaši tagovi:
0
/
4.0
english, 2021
2
Practical Weak Supervision: Doing More with Less Data
O'Reilly Media
Wee Hyong Tok
,
Amit Bahree
,
Senja Filipi
dataset
labeling
functions
snorkel
labels
supervision
function
spark
import
validation
outdoor
models
indoor
classification
fake
classifiers
figure
accuracy
augmentation
datasets
vision
databricks
labeled
scale
dataframe
azure
programming
cluster
abstain
liar
labelmodel
transfer
output
fakenewsnet
transformers
statement
versus
pretrained
resnet
epoch
shown
detection
row
tags
tuning
python
rate
predictions
hugging
lfs
Godina:
2021
Jezik:
english
Fajl:
EPUB, 5.96 MB
Vaši tagovi:
0
/
5.0
english, 2021
3
Practical Weak Supervision: Doing More with Less Data
O'Reilly Media
Wee Hyong Tok
,
Amit Bahree
,
Senja Filipi
labeling
dataset
snorkel
functions
labels
function
supervision
classification
import
spark
models
fake
validation
figure
labeled
accuracy
outdoor
indoor
datasets
databricks
programming
azure
scale
abstain
dataframe
augmentation
vision
cluster
fakenewsnet
output
classifiers
statement
transformers
liar
epoch
shown
row
detection
predictions
pretrained
resnet
lfs
hugging
ktrain
python
rate
tags
labeling_function
labelmodel
pandas
Godina:
2021
Jezik:
english
Fajl:
PDF, 20.76 MB
Vaši tagovi:
0
/
5.0
english, 2021
4
Practical Weak Supervision: Doing More with Less Data - Early Unedited Release
O'Reilly Media, Inc.
Russell Jurney
labeling
dataset
functions
snorkel
labels
function
import
supervision
validation
spark
figure
accuracy
models
fake
classification
outdoor
indoor
abstain
labeled
scale
programming
statement
datasets
output
azure
trained
vision
cluster
epoch
shown
row
databricks
liar
classifiers
augmentation
lfs
labeling_function
content
predictions
false
examples
python
rate
lfanalysis
nlp
pandas
sources
step
tags
coverage
Godina:
2021
Jezik:
english
Fajl:
EPUB, 20.05 MB
Vaši tagovi:
0
/
5.0
english, 2021
1
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