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1
Better Deep Learning: Train Faster, Reduce Overfitting, and Make Better Predictions
machinelearningmastery.com
Jason Brownlee
dataset
function
models
import
neural
accuracy
listing
activation
weights
layer
network
testx
n_train
testy
classification
trainx
trainy
dense
rate
mlp
networks
model.add
output
layers
weight
regularization
verbose
evaluate
input
error
epochs
noise
pyplot.plot
gradient
average
batch
history.history
curves
relu
values
define
dropout
predictions
sequential
range
blobs
test_acc
validation
epoch
algorithm
Tahun:
2018
Bahasa:
english
File:
PDF, 9.42 MB
Tag Anda:
0
/
0
english, 2018
2
Better Deep Learning: Train Faster, Reduce Overfitting, and Make Better Predictions
Machine Learning Mastery
Jason Brownlee
dataset
function
models
import
neural
accuracy
listing
activation
weights
layer
network
testx
n_train
testy
classification
trainx
trainy
dense
rate
mlp
networks
model.add
output
layers
weight
regularization
verbose
evaluate
input
error
epochs
noise
pyplot.plot
gradient
average
batch
history.history
curves
relu
values
define
dropout
predictions
sequential
range
blobs
test_acc
validation
epoch
algorithm
Tahun:
2019
Bahasa:
english
File:
PDF, 9.42 MB
Tag Anda:
5.0
/
5.0
english, 2019
3
Introduction to Time Series Forecasting with Python - How to Prepare Data and Develop Models to Predict the Future. Code
Machine Learning Mastery
Jason Brownlee
import
read_csv
pandas
header
dataframe
index_col
parse_dates
yhat
dataset
range
predictions
rmse
pyplot.show
arima
matplotlib
pyplot
train_size
daily
series.values
residuals
sqrt
mean_squared_error
obs
random
pyplot.plot
diff
values
concat
pyplot.subplot
dataset.csv
temperatures.csv
model_fit
births.csv
total
difference
interval
model.fit
float32
statsmodels.tsa.arima_model
calculate
validation
coef
expected
numpy
predictions.append
evaluate
disp
history.append
sklearn.metrics
split
Tahun:
2020
Bahasa:
english
File:
ZIP, 227 KB
Tag Anda:
0
/
0
english, 2020
1
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