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

## PyTorch basics 

In the previous lessons, we looked at  some Pytorch code, but this time we will take a closer look at it. 
 
[Pytorch](https://pytorch.org/) is an open-source Python deep learning framework that enables us to build and train neural networks. Pytorch is a library that helps you perform mathematical operations between matrices in their basic form because, as you will realize, deep learning is just simple linear algebra.
 
The fundamental building block of a Pytorch is the **tensor**. A tensor is an N-dimensional array. We can have an 1d array (or a vector) `x=[1,2,3,4,5]`, a 2d-array `y=[[1,2],[3,4]]`, and so on. 
 
In Pytorch, these can be defined as: 
 
```python 
X= torch.tensor([ 1,2,3,4,5]) 
Y= torch.tensor([1,2],[3,4]]) 
``` 
From there we can define almost all mathematical operations between tensors.
 
```python 
Z = torch.add(X,Y) 
Z = torch.matmul(X,Y) 
Z = 1 / (1+torch.exp(x)) 
``` 
Let’s revisit the neuron’s equation:
$a =f(w_1*a_1 + w_2*a_2 +  w_3*a_3 + b_o)$ 
 
The above equation can be easily transformed into tensor operations (remember everything in deep learning can be represented as tensors). 
 
`[a_4] = f([ w1, w2, w3] * [a1, a2,a3 ]   +[b_o]) `
 
All we did here is this:
- Gather together all activations into a 1-d vector and all weights into another 1-d vector.
- Multiply them.
- Add the bias.
- Apply the sigmoid function in the result. 

Note that individual numbers can also be seen as 0d tensors. 

Let’s proceed with our first exercise. Using  everything that you learned just now,  try to program your first neuron from scratch. All necessary information and commands have already been mentioned, so all you have to do is reconstruct the above equation using Pytorch.
 
As a first exercise, try to code a simple neuron with three inputs in Pytorch. Initialize the weights as `[0.5,0.5,0.5]`, the bias as 0.5, and return the output. 

  	
 


# PyTorch basics 

In the previous lessons, we looked at  some Pytorch code, but this time we will take a closer look at it. 
 
[Pytorch](https://pytorch.org/) is an open-source Python deep learning framework that enables us to build and train neural networks. Pytorch is a library that helps you perform mathematical operations between matrices in their basic form because, as you will realize, deep learning is just simple linear algebra.
 
The fundamental building block of a Pytorch is the **tensor**. A tensor is an N-dimensional array. We can have an 1d array (or a vector) `x=[1,2,3,4,5]`, a 2d-array `y=[[1,2],[3,4]]`, and so on. 
 
In Pytorch, these can be defined as: 
 
```python 
X= torch.tensor([ 1,2,3,4,5]) 
Y= torch.tensor([1,2],[3,4]]) 
``` 
From there we can define almost all mathematical operations between tensors.
 
```python 
Z = torch.add(X,Y) 
Z = torch.matmul(X,Y) 
Z = 1 / (1+torch.exp(x)) 
``` 
Let’s revisit the neuron’s equation:
$a =f(w_1*a_1 + w_2*a_2 +  w_3*a_3 + b_o)$ 
 
The above equation can be easily transformed into tensor operations (remember everything in deep learning can be represented as tensors). 
 
`[a_4] = f([ w1, w2, w3] * [a1, a2,a3 ]   +[b_o]) `
 
All we did here is this:
- Gather together all activations into a 1-d vector and all weights into another 1-d vector.
- Multiply them.
- Add the bias.
- Apply the sigmoid function in the result. 

Note that individual numbers can also be seen as 0d tensors. 

Let’s proceed with our first exercise. Using  everything that you learned just now,  try to program your first neuron from scratch. All necessary information and commands have already been mentioned, so all you have to do is reconstruct the above equation using Pytorch.
 
As a first exercise, try to code a simple neuron with three inputs in Pytorch. Initialize the weights as `[0.5,0.5,0.5]`, the bias as 0.5, and return the output. 

  	
 


Implement a vanilla neural network from scratch using Pytorch.

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

Build a Neural Network With Pytorch

PyTorch basics