The module focuses on deep learning concepts using different types of neural network implementation.

Neural Networks: Beginners to Advanced

This module 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 module, 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 module.

Neural Networks to Deep Learning

Congratulations! We’ve successfully completed the “Neural Networks to Deep Learning” module.

## Summary
We began by learning the basics of neural networks and their types with practical implementation. The vital concepts that we discussed were neural networks and their testing, convolutional neural networks, recurrent neural networks, autoencoders, generative adversarial networks, attention and transformers, and graph neural networks. 

## What’s next?
Now that we are familiar with the basics of neural networks, it’s time for us to focus more on deep learning.


Congratulations! We’ve successfully completed the “Neural Networks to Deep Learning” module.

# Summary
We began by learning the basics of neural networks and their types with practical implementation. The vital concepts that we discussed were neural networks and their testing, convolutional neural networks, recurrent neural networks, autoencoders, generative adversarial networks, attention and transformers, and graph neural networks. 

# What’s next?
Now that we are familiar with the basics of neural networks, it’s time for us to focus more on deep learning.


Here's a quick summary of the "Neural Networks to Deep Learning" module.

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 Remarks

Summary