Gain insights into basic and intermediate deep learning concepts, including CNNs, RNNs, GANs, and transformers. Delve into fundamental architectures to enhance your machine learning model training skills.

docker.tar.gz

Pytorch

GANS

This course is an accumulation of well-grounded knowledge and experience in deep learning. It provides you with the basic concepts you need in order to start working with and training various machine learning models. 

You will cover both basic and intermediate concepts including but not limited to: convolutional neural networks, recurrent neural networks, generative adversarial networks as well as transformers.

After completing this course, you will have a comprehensive understanding of the fundamental architectural components of deep learning. Whether you’re a data and computer scientist, computer and big data engineer, solution architect, or software engineer, you will benefit from this course.

Introduction to Deep Learning & Neural Networks

# What is a neuron? 

In the world of Neural Networks (NN), the basic building block is the neuron. NNs are nothing more than a collection of neurons organized in layers, with information passing from one layer to the other. So to understand NN, we first need to understand the neuron: the basic computing unit.

Mathematically, we have:

$$y = w_1*x_1 +w_2*x_2+ w_3*x_3$$ 

A neuron is simply  a linear classifier with a single output.

```python
import torch.nn as nn

neuron = nn.Linear(3,1, bias=False) 
```

Looks familiar? In most applications, we also add a bias $b$ to shift the position of the boundary line that separates the data points. This is the infamous Perceptron. 

To extend this idea, we also pass this weighted average through a non-linear function $\sigma$ that will give us the decision boundary.

Why? 

Because with non-linear functions between linear layers, we can model much more complex representations with less linear layers. 

> Non-linearities is a key component that makes NN very rich function approximators.  


Putting it all together, we have:
 
$$ y =\sigma( w_1*x_1 +w_2*x_2+ w_3*x_3+b) = \sigma(w*x+b)$$ 

We organize neurons in layers with ```nn.Linear(in_features, out_features)``` and stack layers in sequential order. The layer’s stacking combined with the non-linear activation function gives us the ability to distinguish non-linearly separable data. 

There are multiple names in the literature for stacking linear layers with non-linear activations: Multi-Layer Perceptron (MLP), artificial Neural Network, and feedforward module. All these terms mean the same thing.

In practice, for three input features and two classes, our model will be like this:


Understand the neural network, how it is formulated, and why it works.

Learn Deep Learning

Neural Networks

Training Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

Autoencoders

Generative Adversarial Networks

Attention and Transformers

Graph Neural Networks

Conclusion

Final Quiz

Neural Networks

What is a neuron?