Data Science — Deep Learning

Neural network basics

import tensorflow as tf
from tensorflow import keras

# Build model
model = keras.Sequential([
    keras.layers.Dense(64, activation='relu', input_shape=(10,)),
    keras.layers.Dense(32, activation='relu'),
    keras.layers.Dense(1, activation='sigmoid')
])

# Compile
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

Training

# Train
history = model.fit(X_train, y_train, epochs=10, validation_split=0.2)

# Evaluate
loss, accuracy = model.evaluate(X_test, y_test)

CNN for images

model = keras.Sequential([
    keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    keras.layers.MaxPooling2D((2, 2)),
    keras.layers.Conv2D(64, (3, 3), activation='relu'),
    keras.layers.Flatten(),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

RNN for sequences

model = keras.Sequential([
    keras.layers.LSTM(64, return_sequences=True, input_shape=(100, 10)),
    keras.layers.LSTM(32),
    keras.layers.Dense(1)
])

Mini Practice

1. Build a simple neural network
2. Train on a dataset
3. Build a CNN
4. Create an RNN

Up Next

Continue with Computer Vision - Image analysis.