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

## Software 

Below, you can find all the libraries that have been used in this course. Feel free to check their documentation for instructions on how to install and use them in your local environment.

- [Pytorch Official documentation](https://pytorch.org/docs/stable/index.html) 
- [Pytorch examples](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html)
- [Numpy](https://numpy.org/)
- [NetworkX](https://networkx.org/)

## Datasets

Throughout the course, we use the following two different open-source datasets for training our models:

- [Cifar10 Dataset](https://www.cs.toronto.edu/~kriz/cifar.html)
- [Open-source graph data from the University of Dortmund](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets) 

## Mathematics
Given below is a list of external links that explain some of the more advanced mathematical terms used.

- [Derivatives](https://en.wikipedia.org/wiki/Partial_derivative)
- [Chain rule](https://en.wikipedia.org/wiki/Chain_rule)
- [Probability distributions](https://en.wikipedia.org/wiki/Probability_distribution)
- [Conditional probabilities](https://en.wikipedia.org/wiki/Conditional_probability)
- [Expected value](https://en.wikipedia.org/wiki/Expected_value)
- [Kullback–Leibler divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence)
- [Min-max games](https://en.wikipedia.org/wiki/Minimax)
- [Singular Value Decomposition](https://en.wikipedia.org/wiki/Singular_value_decomposition)
- [Eigenvalues and Eigenvectors](https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors)
- [Laplacian matrix](https://en.wikipedia.org/wiki/Laplacian_matrix)
- [Chebyshev approximation](https://en.wikipedia.org/wiki/Approximation_theory)
 





# Software 

Below, you can find all the libraries that have been used in this course. Feel free to check their documentation for instructions on how to install and use them in your local environment.

- [Pytorch Official documentation](https://pytorch.org/docs/stable/index.html) 
- [Pytorch examples](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html)
- [Numpy](https://numpy.org/)
- [NetworkX](https://networkx.org/)

# Datasets

Throughout the course, we use the following two different open-source datasets for training our models:

- [Cifar10 Dataset](https://www.cs.toronto.edu/~kriz/cifar.html)
- [Open-source graph data from the University of Dortmund](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets) 

# Mathematics
Given below is a list of external links that explain some of the more advanced mathematical terms used.

- [Derivatives](https://en.wikipedia.org/wiki/Partial_derivative)
- [Chain rule](https://en.wikipedia.org/wiki/Chain_rule)
- [Probability distributions](https://en.wikipedia.org/wiki/Probability_distribution)
- [Conditional probabilities](https://en.wikipedia.org/wiki/Conditional_probability)
- [Expected value](https://en.wikipedia.org/wiki/Expected_value)
- [Kullback–Leibler divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence)
- [Min-max games](https://en.wikipedia.org/wiki/Minimax)
- [Singular Value Decomposition](https://en.wikipedia.org/wiki/Singular_value_decomposition)
- [Eigenvalues and Eigenvectors](https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors)
- [Laplacian matrix](https://en.wikipedia.org/wiki/Laplacian_matrix)
- [Chebyshev approximation](https://en.wikipedia.org/wiki/Approximation_theory)
 





Find links to external resources that will solidify your understanding and answer any questions that you might have.

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

Appendix

Software