Skip to content

Part II: Tensor Mechanics and Computation

“Before models can learn, data must be shaped, sliced, and understood.”

In Part II, you move from simply knowing what a tensor is to mastering how to manipulate, structure, and compute with them efficiently. This section gives you the foundational mechanics required to build, debug, and optimize any deep learning pipeline.

These chapters are not flashy—but they’re absolutely essential. Every model, from CNNs to Transformers, starts with clean tensor ops under the hood.

✅ Chapter 6: Tensor Indexing & Reshaping

You’ll learn how to slice and reshape tensors like a ninja: selecting rows, flattening shapes, adding batch dimensions, and preparing inputs for layers.

✅ Chapter 7: Tensor Broadcasting

One of TensorFlow’s most elegant features. This chapter demystifies how tensors of different shapes are automatically aligned and expanded during element-wise operations—no boilerplate reshaping required.

✅ Chapter 8: Ragged, Sparse, and String Tensors

Not all tensors are pretty. You’ll work with jagged text sequences (ragged), memory-efficient structures (sparse), and string tensors for natural language processing—crucial for real-world data.

✅ Chapter 9: Variables & Trainable Parameters

Meet tf.Variable, the mutable tensor that learns. You'll explore how TensorFlow stores, updates, and tracks parameters across training steps.

✅ Chapter 10: Automatic Differentiation (tf.GradientTape)

This is the engine behind learning. You'll record forward passes and compute gradients for optimization using TensorFlow's autodiff system—step by step.

✅ Chapter 11: Graphs & Functions (@tf.function)

Performance mode, engaged. You’ll convert Python into TensorFlow graphs, improving speed and enabling deployment to GPUs, TPUs, and production systems.

✅ Chapter 12: Bitwise & Numerical Operations

The finishing move. Learn advanced operations like clipping, rounding, modulo, one-hot encoding, and bitwise ops—used for loss design, masking, and precision tuning.

After Part II, You Will Be Able To:

  • Slice, reshape, and broadcast tensors confidently

  • Work with variable-length and sparse data

  • Write training loops that compute and apply gradients

  • Build efficient TensorFlow graphs for speed

  • Understand the math operations that power model internals

Part II gives you the raw power of TensorFlow. Part III shows you how to channel it into models.