diff --git a/intermediate_source/parametrizations.py b/intermediate_source/parametrizations.py
index 59cff1d241c..13fbf625d5c 100644
--- a/intermediate_source/parametrizations.py
+++ b/intermediate_source/parametrizations.py
@@ -4,6 +4,45 @@
 =========================
 **Author**: `Mario Lezcano <https://github.com/lezcano>`_
 
+What is a Parametrization?
+--------------------------
+
+In the context of neural networks, a **parametrization** is a transformation applied
+to the parameters (weights and biases) of a model before they are used in computations.
+Rather than using parameters directly as learned during training, we transform them
+through a function to enforce certain mathematical properties or constraints.
+
+**Why do we need parametrizations?**
+
+When training neural networks, we often want parameters to satisfy specific constraints:
+
+- **Orthogonal matrices** for stable recurrent networks
+- **Positive definite matrices** for covariance estimation
+- **Normalized weights** for improved training dynamics
+- **Sparse or structured weights** for model compression
+
+Without parametrizations, enforcing these constraints is challenging. We could use
+penalty methods (adding constraint violations to the loss), but these often fail in
+deep learning due to the complexity of the optimization landscape.
+
+**What problems do parametrizations solve?**
+
+1. **Hard constraints**: Parametrizations guarantee that constraints are always satisfied,
+   not just approximately. If you parametrize a weight to be orthogonal, it will be
+   orthogonal at every step of training.
+
+2. **Improved optimization**: By transforming the parameter space, we can make
+   optimization easier. For example, learning the norm and direction of a weight
+   separately (weight normalization) can accelerate training.
+
+3. **Regularization**: Constraining parameters can act as a powerful regularizer,
+   preventing overfitting and improving generalization.
+
+4. **Numerical stability**: Certain parametrizations (like orthogonal matrices in RNNs)
+   prevent gradient explosion/vanishing, making training more stable.
+
+**Real-world motivation**
+
 Regularizing deep-learning models is a surprisingly challenging task.
 Classical techniques such as penalty methods often fall short when applied
 on deep models due to the complexity of the function being optimized.
@@ -37,6 +76,19 @@
 In this tutorial, you will learn how to implement and use this pattern to put
 constraints on your model. Doing so is as easy as writing your own ``nn.Module``.
 
+**Further Reading**
+
+For those interested in diving deeper into parametrizations and their applications:
+
+- `Weight Normalization (Salimans & Kingma, 2016) <https://arxiv.org/abs/1602.07868>`_:
+  Introduces weight normalization for accelerating training.
+- `Spectral Normalization for GANs (Miyato et al., 2018) <https://arxiv.org/abs/1802.05957>`_:
+  Proposes spectral normalization to stabilize GAN training.
+- `Orthogonal RNNs (Helfrich et al., 2018) <https://arxiv.org/abs/1803.00254>`_:
+  Explores orthogonal parametrizations for recurrent networks.
+- `Riemannian Optimization (Absil et al., 2008) <https://press.princeton.edu/absil>`_:
+  Mathematical foundations for optimization on manifolds with constraints.
+
 Requirements: ``torch>=1.9.0``
 
 Implementing parametrizations by hand