Master Deep Learning

1. Neural Network Fundamentals

Understand how artificial neurons work and build your first neural network from scratch.

# Simple neural network with TensorFlow
import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation='relu', input_shape=(784,)),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

2. Convolutional Neural Networks (CNNs)

Learn to process images and build computer vision models.

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

3. Recurrent Neural Networks (RNNs)

Process sequential data with LSTM and GRU networks.

# LSTM for sequence prediction
model = tf.keras.Sequential([
    tf.keras.layers.LSTM(50, return_sequences=True, input_shape=(timesteps, features)),
    tf.keras.layers.LSTM(50),
    tf.keras.layers.Dense(1)
])

4. Transfer Learning

Leverage pre-trained models for faster development.

# Transfer learning with pre-trained model
base_model = tf.keras.applications.VGG16(
    weights='imagenet',
    include_top=False,
    input_shape=(224, 224, 3)
)
base_model.trainable = False

model = tf.keras.Sequential([
    base_model,
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(num_classes, activation='softmax')
])