Deep Learning with TensorFlow: The Complete Guide

Core Concepts:

• Tensors: N-dimensional arrays (tf.Tensor)
• Graph Execution: Define-and-run paradigm
• Eager Mode: Immediate execution (default in TF 2.x)
• Keras API: High-level model building

Basic Operations:

import tensorflow as tf

# Create tensors
a = tf.constant([[1, 2], [3, 4]])
b = tf.constant([[5, 6], [7, 8]])

# Matrix multiplication
c = tf.matmul(a, b)  # [[19, 22], [43, 50]]

# Automatic differentiation
with tf.GradientTape() as tape:
    tape.watch(a)
    y = tf.reduce_sum(a * 2)
grad = tape.gradient(y, a)  # [[2, 2], [2, 2]]
        

Model Building Approaches:

CNN Example:

from tensorflow.keras import layers, models

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

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

Production Workflow:

End-to-End Example:

# Create dataset pipeline
train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train))
train_ds = train_ds.shuffle(1000).batch(32).prefetch(tf.data.AUTOTUNE)

# Train with callbacks
model.fit(train_ds, epochs=10,
          callbacks=[
              tf.keras.callbacks.ModelCheckpoint('model.keras'),
              tf.keras.callbacks.EarlyStopping(patience=3)
          ])

# Export for serving
tf.saved_model.save(model, 'saved_model')
        

Cutting-Edge Features:

• Custom Training Loops: Fine-grained control
• Mixed Precision: FP16/FP32 training
• Distributed Strategies: Multi-worker training
• TF Hub: Pretrained models

Custom Training Example:

@tf.function  # Graph compilation
def train_step(x, y):
    with tf.GradientTape() as tape:
        preds = model(x, training=True)
        loss = loss_fn(y, preds)
    grads = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(grads, model.trainable_variables))
    return loss

for epoch in range(epochs):
    for x_batch, y_batch in train_ds:
        loss = train_step(x_batch, y_batch)
        tf.summary.scalar('loss', loss, step=optimizer.iterations)