diff --git a/3-NeuralNetworks/README.md b/3-NeuralNetworks/README.md index 14943044..9391e1b4 100644 --- a/3-NeuralNetworks/README.md +++ b/3-NeuralNetworks/README.md @@ -2,23 +2,25 @@ ![Summary of Intro Neural Networks content in a doodle](../sketchnotes/ai-neuralnetworks.png) -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 X1, ..., XN and an output Y, and a series of weights W1, ..., WN. An output is calculated as +Thus, the simplest mathematical model of a neuron contains several inputs X1, ..., XN and an output Y, and a series of weights W1, ..., WN. An output is calculated as: Y = f\left(\sum_{i=1}^N X_iW_i\right) 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)