Sinir Ağları

Python ile Finans için Machine Learning

Nathan George

Data Science Professor

GPU moore's law

Sinir ağlarının potansiyeli var

Sinir ağları şunlara sahiptir:

doğrusal olmama
değişken etkileşimleri
özelleştirilebilirlik

basic neural net diagram

net diagram with basic math

neural net math

activation neural net diagram

activation function

loss function

forward pass

backwards pass

keras and tensorflow logos

Keras ile sinir ağı uygulamak

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

Keras ile sinir ağı uygulamak

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense


model = Sequential()

model.add(Dense(50,
                input_dim=scaled_train_features.shape[1],
                activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(1, activation='linear'))

Modeli eğitmek

model.compile(optimizer='adam', loss='mse')
history = model.fit(scaled_train_features, 
                    train_targets, 
                    epochs=50)

plt.plot(history.history['loss'])
plt.title('loss:' + str(round(history.history['loss'][-1], 6)))
plt.xlabel('epoch')
plt.ylabel('loss')
plt.show()

loss plot

Performansı kontrol et

from sklearn.metrics import r2_score

# calculate R^2 score
train_preds = model.predict(scaled_train_features)
print(r2_score(train_targets, train_preds))

0.4771387560719418

Performansı görselleştir

# plot predictions vs actual
plt.scatter(train_preds, train_targets)
plt.xlabel('predictions')
plt.ylabel('actual')
plt.show()

preds vs actual

Bir sinir ağı kur!

Python ile Finans için Machine Learning