TensorFlow: Core Concepts & Tensors

TensorFlow is Google's open-source machine learning framework. TF 2.x uses eager execution by default (compute immediately) and Keras as its primary high-level API. Used for neural networks, computer vision, NLP, and time-series.

TensorFlow vs PyTorch

• TensorFlow: better for production deployment (TFLite, TF Serving, TF.js), stronger mobile support, TensorBoard built-in

• PyTorch: more pythonic, preferred in research, easier debugging, dynamic computation graph

• Both have converged significantly — Keras 3 supports both backends

• JAX: growing alternative (Google), functional style, excellent for custom gradients

Tensors

import tensorflow as tf
import numpy as np

# Create tensors
scalar = tf.constant(3.14)                     # rank-0 tensor
vector = tf.constant([1.0, 2.0, 3.0])         # rank-1, shape (3,)
matrix = tf.constant([[1, 2], [3, 4]])         # rank-2, shape (2, 2)
tensor3d = tf.zeros([batch, height, width])    # shape (b, h, w)

# Tensor properties
matrix.shape     # TensorShape([2, 2])
matrix.dtype     # tf.int32
matrix.numpy()   # convert to numpy array

# Operations (eager by default in TF2)
a = tf.constant([[1.0, 2.0], [3.0, 4.0]])
b = tf.constant([[5.0, 6.0], [7.0, 8.0]])

tf.add(a, b)          # element-wise addition
tf.matmul(a, b)       # matrix multiplication
tf.reduce_sum(a)      # sum all elements
tf.reduce_mean(a, axis=0)  # mean along axis
tf.transpose(a)
tf.reshape(a, [4])    # flatten to [4]

# Variables (trainable parameters)
w = tf.Variable([[0.1, 0.2], [0.3, 0.4]], trainable=True)
w.assign(new_value)
w.assign_add(delta)

Automatic Differentiation

# GradientTape — compute gradients
x = tf.Variable(3.0)

with tf.GradientTape() as tape:
    y = x ** 2 + 2 * x + 1   # y = x² + 2x + 1

dy_dx = tape.gradient(y, x)  # dy/dx = 2x + 2 = 8.0

# Multiple variables
w = tf.Variable(2.0)
b = tf.Variable(1.0)

with tf.GradientTape() as tape:
    y_pred = w * x + b
    loss = (y_pred - 5.0) ** 2

grads = tape.gradient(loss, [w, b])  # [dL/dw, dL/db]

Data Pipelines with tf.data

import tensorflow as tf

# From numpy/lists
dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))

# Efficient pipeline
dataset = (
    dataset
    .shuffle(buffer_size=1000, seed=42)
    .batch(32)
    .prefetch(tf.data.AUTOTUNE)   # overlap preprocessing with training
)

# From files
image_dataset = tf.keras.utils.image_dataset_from_directory(
    'data/images/',
    image_size=(224, 224),
    batch_size=32,
    validation_split=0.2,
    subset='training',
    seed=42,
)

# Map preprocessing
def preprocess(image, label):
    image = tf.cast(image, tf.float32) / 255.0
    return image, label

dataset = dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)