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

## What is deep learning?
Deep learning is considered a subfield of machine learning. Even though there are countless inspirations from real neurons, we will focus on modeling everything with formulas, intuitions, and theories that actually work.

In practice, deep learning is the scaling up of computational structures called neural networks. 

Why do we take the time to develop such approaches?

Because it is the optimal solution when working with really large-scale data right now.

> It is important to keep  in mind that deep learning is all about learning **powerful** representations.

There is a huge shift from extracting features to learning features, and that  is what deep learning is all about.

## Deep learning applications
You will get a general perspective of a huge variety of problems that you can solve with deep learning.

First, you will learn to formulate problems in terms of machine and deep learning. That's a crucial skill that you will use throughout your career and projects.

Secondly, you will learn the most basic components that tackle some of the following tasks:

- Image classification
- Time series prediction
- Image denoising and compression
- Image generation
- Machine translation
- Graph and node classification

Deep learning has already transformed a variety of **businesses** such as web search, augmented reality, social networks, automobiles, retail, cybersecurity, and manufacturing. But the most exciting thing is the potential novel applications that may appear in the future. These projects can radically transform every industry.

Some experts claim that **AI is the new electricity.** While this may be a disputable idea, what is for certain  is that **deep learning is one of the most sought after and well-paid skills.**

So why stay behind?




# What is deep learning?
Deep learning is considered a subfield of machine learning. Even though there are countless inspirations from real neurons, we will focus on modeling everything with formulas, intuitions, and theories that actually work.

In practice, deep learning is the scaling up of computational structures called neural networks. 

Why do we take the time to develop such approaches?

Because it is the optimal solution when working with really large-scale data right now.

> It is important to keep  in mind that deep learning is all about learning **powerful** representations.

There is a huge shift from extracting features to learning features, and that  is what deep learning is all about.

# Deep learning applications
You will get a general perspective of a huge variety of problems that you can solve with deep learning.

First, you will learn to formulate problems in terms of machine and deep learning. That's a crucial skill that you will use throughout your career and projects.

Secondly, you will learn the most basic components that tackle some of the following tasks:

- Image classification
- Time series prediction
- Image denoising and compression
- Image generation
- Machine translation
- Graph and node classification

Deep learning has already transformed a variety of **businesses** such as web search, augmented reality, social networks, automobiles, retail, cybersecurity, and manufacturing. But the most exciting thing is the potential novel applications that may appear in the future. These projects can radically transform every industry.

Some experts claim that **AI is the new electricity.** While this may be a disputable idea, what is for certain  is that **deep learning is one of the most sought after and well-paid skills.**

So why stay behind?




Get introduced to deep learning and learn why you should consider studying and/or pursuing it.

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

Why Learn Deep Learning?

What is deep learning?