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Differences between deep learning and neural networks 2022 Era!

 deep learning

You may have heard more and more about the terms deep learning and neural networks. On the other hand, many people do not know the difference between the two.

Therefore, in this article, we will introduce the differences and types of relationships between deep learning and neural networks.

Table of Contents

  • Difference Between Deep Learning and Neural Networks
    • Neural networks are a form of machine learning
    • Deep learning is based on neural networks
  • Types of typical neural networks
    • auto encoder
    • Convolutional Neural Network (CNN)
    • Recurrent Neural Network (RNN)
    • LSTMs
    • Generative Adversarial Network (GAN)
  • What is DNN developed from neural network?
  • Books to learn about neural networks
    • 1 Introduction to creating your own neural network
    • 2 Neural network theory and implementation that can be understood only by the necessary mathematics
  • Books to learn about deep learning
    • 1GAN Deep Learning Implementation Handbook
    • 2 Introduction to Object/Image Recognition and Time Series Data Processing [2nd Edition for TensorFlow2/PyTorch]
  • Deep learning related qualifications & certifications 
  • Deep learning trends
  • summary

Difference Between Deep Learning and Neural Networks

Here, I will specifically explain how deep learning and neural networks differ from the relationship between the two.

Neural networks are a form of machine learning

First, there is machine learning as a method for realizing human learning ability in computers. Neural networks are one of the algorithms that make this machine learning work.

Difference from AI

In addition, among the specific learning methods for realizing AI, machine learning is a method that attempts to achieve human learning and prediction capabilities with computers.

Therefore, the relationship between AI, neural networks , and deep learning is as follows.

Deep learning is based on neural networks

The application of neural networks is called deep learning, or deep learning. In other words, deep learning is included in the neural network of machine learning.

Neural networks are based on deep learning.

Types of typical neural networks

There are also different types of neural networks. Here, we introduce some of the most popular neural networks.

Auto encoder

An autoencoder is an algorithm that compresses the input data once, leaves only the important features, and then restores them to the original dimension.

Deep learning has a structure made up of many layers of this autoencoder. So the mechanics of autoencoders are similar to the mechanics of deep learning.

The input layer nodes receive the data and compress it into the hidden layer. This process is called weighting, and the data are scored according to their importance. At this time, data with low scores are excluded. This is the encoding.

The data is also weighted as it goes to the output layer, and the final value is the sum of the data received by the node from multiple edges. This is called decoding.

Convolutional Neural Network (CNN)

A convolutional neural network (CNN) refers to a neural network structure with an operation called “convolution”.

Convolution is a layer that extracts features locally in the image you want to identify. There are differences in feature values ​​depending on the size and value of the filter.

Recurrent Neural Network (RNN)

A recurrent neural network (RNN) is a neural network with cells attached so that it can handle time series data.

When AI learns the sentence “I am XX” in sentence generation, the AI ​​calls “I am”, and the data in which past data affects future data is called time-series data.

LSTMs

LSTM (Long short-term memory) is one of the models for time series data that appeared in 1995 as an extension of RNN. It is realized by replacing the middle layer unit of RNN with a block with memory and three gates called LSTM block.

It is now possible to learn long-term dependencies, which was not possible with RNN.

Generative Adversarial Network (GAN)

It is called a generative adversarial network because it deepens the learning of input data by making two neural networks compete with each other. A model that generates new pseudo data from input data and images.

With this model, new images and sounds can be generated and converted from low quality images to high quality images.

What is DNN developed from neural network?

DNN stands for Deep Neural Network and is one of the deep learning methods.

By utilizing deep learning using DNN, it is now possible to perform more complex processing.

Deep learning was originally based on neural networks. However, conventional neural networks basically consisted of three layers: an input layer, an intermediate layer, and an output layer. .

Therefore, DNN, which is designed to be able to recognize even complicated neural network patterns, has appeared and solved this problem.

Books to learn about neural networks

You can learn more about neural networks in books.

1 Introduction to creating your own neural network

You can learn how to create your own neural network using Python while learning mathematics step by step. In addition, even if you do not have knowledge of mathematics beyond what you learned in junior high school and high school, you can understand it.

2 Neural network theory and implementation that can be understood only by the necessary mathematics

While following the history of deep learning development, the mechanism and features are explained in detail. This is the perfect book for those who want to acquire basic knowledge of artificial intelligence.

Books to learn about deep learning

Next, I will introduce two books where you can learn deep learning.

1GAN Deep Learning Implementation Handbook

This is a GAN (Generative Adversarial Network) tutorial in Python. It covers everything from the basics of GANs to applications such as image generation, super-resolution, domain conversion, video conversion, style manipulation, and anomaly detection 3D data generation.

2 Introduction to Object/Image Recognition and Time Series Data Processing [2nd Edition for TensorFlow2/PyTorch]

You can enjoy learning about basic deep learning theory, two-dimensional filters, transfer learning, time series data, natural language processing, and more. It is therefore ideal as an introductory text.

Deep learning related qualifications & certifications 

For those who want to know about deep learning, one way is to start studying related qualifications and certifications.

The most famous certifications and certifications related to deep learning are the G and E certifications sponsored by the Japan Deep Learning Association. In addition, there are various other certifications and certifications, so it would be a good idea to look for the certification that suits your purpose.

Deep learning trends

In the future, it is expected that the introduction will accelerate as a solution to social problems such as labor shortages, work style reforms, and aging infrastructure. As a result, the market for image recognition using deep learning is likely to expand.

If you have knowledge of deep learning, you may find more opportunities to find a job, change jobs, or even in a company.

Therefore, it can be said that it is important to acquire knowledge about deep learning.

Summary

In this article, I have explained the differences, relationships, and types of deep learning and neural networks.

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