Surviving a meteor strike

Introduction to Deep Learning with Keras

Miguel Esteban

Data Scientist & Founder

Recap

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

# Create a new sequential model
model = Sequential()

# Add and input and dense layer
model.add(Dense(2, input_shape=(3,),
                activation="relu"))

# Add a final 1 neuron layer
model.add(Dense(1))
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Introduction to Deep Learning with Keras

Compiling

# Compiling your previously built model
model.compile(optimizer="adam", loss="mse")

Introduction to Deep Learning with Keras

Training

# Train your model
model.fit(X_train, y_train, epochs=5)

Epoch 1/5
1000/1000 [==============================] - 0s 242us/step - loss: 0.4090
Epoch 2/5
1000/1000 [==============================] - 0s 34us/step - loss: 0.3602
Epoch 3/5
1000/1000 [==============================] - 0s 37us/step - loss: 0.3223
Epoch 4/5
1000/1000 [==============================] - 0s 34us/step - loss: 0.2958
Epoch 5/5
1000/1000 [==============================] - 0s 33us/step - loss: 0.2795
Introduction to Deep Learning with Keras

Predicting

# Predict on new data
preds = model.predict(X_test)

# Look at the predictions
print(preds)

array([[0.6131608 ],
       [0.5175948 ],
       [0.60209155],
       ...,
       [0.55633   ],
       [0.5305591 ],
       [0.50682044]])
Introduction to Deep Learning with Keras

Evaluating

# Evaluate your results
model.evaluate(X_test, y_test)

1000/1000 [==============================] - 0s 53us/step
0.25
Introduction to Deep Learning with Keras

The problem at hand

Introduction to Deep Learning with Keras

Scientific prediction

Introduction to Deep Learning with Keras

Your task

Introduction to Deep Learning with Keras

Let's save the earth!

Introduction to Deep Learning with Keras

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