March 2019
Intermediate to advanced
576 pages
20h 51m
Portuguese (Portugal, Brazil)
Content preview from Mãos à Obra: Aprendizado de Máquina com Scikit-Learn e TensorFlow
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Regressão Logística | 139
# prepare os dados
poly_scaler = Pipeline([
("poly_features", PolynomialFeatures(degree=90, include_bias=False)),
("std_scaler", StandardScaler()) ])
X_train_poly_scaled = poly_scaler.fit_transform(X_train)
X_val_poly_scaled = poly_scaler.transform(X_val)
sgd_reg = SGDRegressor(n_iter=1, warm_start=True, penalty=None,
learning_rate="constant", eta0=0.0005)
minimum_val_error = float("inf")
best_epoch = None
best_model = None
for epoch in range(1000):
sgd_reg.fit(X_train_poly_scaled, y_train) # continua de onde parou
y_val_predict = sgd_reg.predict(X_val_poly_scaled)
val_error = mean_squared_error(y_val_predict, y_val)
if val_error ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.