Deep Learning with TensorFlow: The Complete Guide

TensorFlow powers 78% of production deep learning models (Google Cloud, 2024). This tutorial covers everything from basic operations to deploying production models with TensorFlow 2.x and Keras API.

TensorFlow Usage Distribution (2024)

Computer Vision (35%)

NLP (30%)

Recommendation Systems (20%)

Other (15%)

1. TensorFlow Fundamentals

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]]

2. Building Models with Keras

Model Building Approaches:

Method	Example	When to Use
Sequential API	model = Sequential([layers...])	Simple stacks
Functional API	inputs = Input(); x = Dense()(inputs)	Complex architectures
Model Subclassing	class MyModel(Model):...	Custom implementations

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'])

3. Training & Deployment

Production Workflow:

Data Pipeline

tf.data.Dataset

Optimized loading

Distributed Training

MirroredStrategy

Multi-GPU

Model Serving

TF Serving

Low-latency

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')

TensorFlow Ecosystem Tools

Tool	Purpose	Key Feature
TensorFlow Lite	Mobile/IoT	Quantization
TensorFlow.js	Browser	WebGL acceleration
TFX	ML Pipelines	End-to-end orchestration
TensorBoard	Visualization	Training metrics

4. Advanced Techniques

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

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