**Sequential Model Creation - tf.keras.Sequential**:

Example:

```
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),
tf.keras.layers.Dense(10, activation='softmax')
])
```

**Output**:

Creating a sequential model with two dense layers for a simple feedforward neural network.

**Functional API Model Building**:

Example:

```
inputs = tf.keras.layers.Input(shape=(784,))
x = tf.keras.layers.Dense(64, activation='relu')(inputs)
outputs = tf.keras.layers.Dense(10, activation='softmax')(x)
model = tf.keras.Model(inputs, outputs)
```

**Output**:

Building a model using the functional API for greater flexibility.

**Custom Layer Creation - Subclassing tf.keras.layers.Layer**:

Example:

```
class MyDenseLayer(tf.keras.layers.Layer):
def __init__(self, units):
super(MyDenseLayer, self).__init__()
self.units = units
def build(self, input_shape):
self.w = self.add_weight("weights", shape=(input_shape[-1], self.units))
self.b = self.add_weight("bias", shape=(self.units,))
def call(self, inputs):
return tf.matmul(inputs, self.w) + self.b
my_layer = MyDenseLayer(64)
```

**Output**:

Creating a custom layer by subclassing tf.keras.layers.Layer.

**Model Compilation - model.compile**:

Example:

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

**Output**:

Compiling the model with an optimizer, loss function, and evaluation metrics.

**Model Summary - model.summary()**:

Example:

```
model.summary()
```

**Output**:

Displaying a summary of the model architecture with layer names, output shapes, and parameter counts.

**Model Visualization - tf.keras.utils.plot_model**:

Example:

```
tf.keras.utils.plot_model(model, to_file='model.png', show_shapes=True)
```

**Output**:

Generating a graphical visualization of the model architecture.

**Model Loading - tf.keras.models.load_model**:

Example:

```
loaded_model = tf.keras.models.load_model('saved_model.h5')
```

**Output**:

Loading a pre-trained model from a saved model file.

**Model Training - model.fit**:

Example:

```
history = model.fit(train_data, train_labels, epochs=10, validation_data=(val_data, val_labels))
```

**Output**:

Training the model using provided data and monitoring training progress and validation metrics.

**Fine-Tuning Pretrained Models - Transfer Learning**:

Example:

```
base_model = tf.keras.applications.MobileNetV2(weights='imagenet', include_top=False)
base_model.trainable = False # Freeze base model layers
inputs = tf.keras.layers.Input(shape=(224, 224, 3))
x = base_model(inputs)
outputs = tf.keras.layers.Dense(10, activation='softmax')(x)
model = tf.keras.Model(inputs, outputs)
```

**Output**:

Building a new model by fine-tuning a pretrained MobileNetV2 model for a specific task.

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