diff --git a/3-NeuralNetworks/README.md b/3-NeuralNetworks/README.md
index 14943044994c3cf1635aa56fcad9f4ac3dcc289c..9391e1b4bef2512965589308878df4c86ec4a7a6 100644
--- a/3-NeuralNetworks/README.md
+++ b/3-NeuralNetworks/README.md
@@ -2,23 +2,25 @@
-As we have discussed in the introduction, one of the ways to achieve intelligence is to train a **computer model** or an **artificial brain**. Since the middle of 20th century, researchers tried different mathematical models, until in recent years this direction proved to by hugely successful. Such mathematical models of the brain are called **Neural Networks** (sometimes *Artificial Neural Networks*, ANNs, in order to indicate that we are talking about models, not real networks of neurons).
+As we discussed in the introduction, one of the ways to achieve intelligence is to train a **computer model** or an **artificial brain**. Since the middle of 20th century, researchers tried different mathematical models, until in recent years this direction proved to by hugely successful. Such mathematical models of the brain are called **neural networks**.
+
+> Sometimes neural networks are called *Artificial Neural Networks*, ANNs, in order to indicate that we are talking about models, not real networks of neurons.
## Machine Learning
-Neural Networks are a part of larger discipline called **Machine Learning**, whose goal is to use data to train computer models that are able to solve problems. Machine Learning constitutes a large part of Artificial Intelligence, however, we do not cover classical ML in this curricula.
+Neural Networks are a part of a larger discipline called **Machine Learning**, whose goal is to use data to train computer models that are able to solve problems. Machine Learning constitutes a large part of Artificial Intelligence, however, we do not cover classical ML in this curricula.
-> Visit our separate **[Machine Learning for Beginners](http://github.com/microsoft/ml-for-beginners)** curriculum to learn more about Machine Learning.
+> Visit our separate **[Machine Learning for Beginners](http://github.com/microsoft/ml-for-beginners)** curriculum to learn more about classic Machine Learning.
In Machine Learning, we assume that we have some dataset of examples **X**, and corresponding output values **Y**. Examples are often N-dimensional vectors that consist of **features**, and outputs are called **labels**.
-We will consider two most common machine learning problems:
+We will consider the two most common machine learning problems:
* **Classification**, where we need to classify an input object into two or more classes.
* **Regression**, where we need to predict a numerical number for each of the input samples.
-> When representing inputs and outputs as tensors, input dataset is a matrix of size M×N, where M is number of samples, N is the number of features. Output labels Y is the vector of size M.
+> When representing inputs and outputs as tensors, the input dataset is a matrix of size M×N, where M is number of samples and N is the number of features. Output labels Y is the vector of size M.
-In this curricula, we will only focus on neural network models.
+In this curriculum, we will only focus on neural network models.
## A Model of a Neuron
@@ -28,19 +30,19 @@ From biology we know that our brain consists of neural cells, each of them havin
----|----
Real Neuron *([Image](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) from Wikipedia)* | Artificial Neuron *(Image by Author)*
-Thus, simplest mathematical model of a neuron contains several inputs X<sub>1</sub>, ..., X<sub>N</sub> and an output Y, and a series of weights W<sub>1</sub>, ..., W<sub>N</sub>. An output is calculated as
+Thus, the simplest mathematical model of a neuron contains several inputs X<sub>1</sub>, ..., X<sub>N</sub> and an output Y, and a series of weights W<sub>1</sub>, ..., W<sub>N</sub>. An output is calculated as:
<img src="images/netout.png" alt="Y = f\left(\sum_{i=1}^N X_iW_i\right)" width="131" height="53" align="center"/>
where f is some non-linear **activation function**.
-> Early models of neuron were described in classical paper [A logical calculus of the ideas immanent in nervous activity](http://www.springerlink.com/content/61446605110620kg/fulltext.pdf) by Warren McCullock and Walter Pitts in 1943. Donald Hebb in his book "[The Organization of Behavior: A Neuropsychological Theory](https://books.google.com/books?id=VNetYrB8EBoC)" proposed the way those networks can be trained.
+> Early models of neuron were described in the classical paper [A logical calculus of the ideas immanent in nervous activity](http://www.springerlink.com/content/61446605110620kg/fulltext.pdf) by Warren McCullock and Walter Pitts in 1943. Donald Hebb in his book "[The Organization of Behavior: A Neuropsychological Theory](https://books.google.com/books?id=VNetYrB8EBoC)" proposed the way those networks can be trained.
## In this Section
In this section we will learn about:
* [Perceptron](03-Perceptron/README.md), one of the earliest neural network models for two-class classification
-* [Multi-layered networks](04-OwnFramework/README.md) and [how to build our own framework](04-OwnFramework/OwnFramework.ipynb)
-* [Neural Network Frameworks](05-Frameworks/README.md), such as [PyTorch](05-Frameworks/IntroPyTorch.ipynb) and [Keras/TensorFlow](05-Frameworks/IntroKerasTF.ipynb)
+* [Multi-layered networks](04-OwnFramework/README.md) with a paired notebook [how to build our own framework](04-OwnFramework/OwnFramework.ipynb)
+* [Neural Network Frameworks](05-Frameworks/README.md), with these notebooks: [PyTorch](05-Frameworks/IntroPyTorch.ipynb) and [Keras/Tensorflow](05-Frameworks/IntroKerasTF.ipynb)
* [Overfitting](05-Frameworks/Overfitting.md)