Last Updated on April 8, 2021
If you are preparing for Artificial intelligence or data science, you must have familiar with machine learning, deep learning, neural network, etc. But what happen most of the time, people use deep learning and neural network interchangeably. Yes, of course, we learn about the neural networks in deep learning. But it does not mean that both terms are the same. In this article, we talk about Neural network vs Deep learning.
Read the complete article because their difference may little confuse you. So, read it carefully.
Before we tell you the difference, let’s know what they define.
What is Deep learning?
Deep learning is the subset of machine learning where machines learn by themselves by simulating the human brain. It involves learning through the layers.
The deep learning model learns to perform tasks from text, sound, images and achieves more accuracy than a neural network. You can also say that deep learning is the up-gradation of neural networks.
What is Neural Network?
The neural network was derived in 1944 by Warren McCullough and Walter Pitts. They describe a neural network as the single cell in the network of cells that receives input, processes those inputs, and generates output. It comprises three layers- i) Input layer ii)Hidden layer iii) Output layer.
- Input layer:- It receives input in various forms like images, text, sound, etc.
- Hidden layer:- In the hidden layer, the function applies weights to the input and directs them as output.
- Output layer:- It is used to get the output result(final layer).
In the 1980s-2000s, the neural networks had only 1 input layer, 1 hidden layer, and 1 output layer. At that time, there were no applications built using neural networks. But around 2008-2012, the market of the neural network get hiked due to deep learning because it increases the number of layers that process even large datasets and predict output with high accuracy.
No doubt, the neural network is the root of deep learning. But we are learning neural networks today because of deep learning. The things that differentiate between them are accuracy, the time needed to give output, and many more.
Best book for Deep learning
If you want to explore more about deep learning, I would strongly recommend you to read one book i.e. Deep Learning by Ian Goodfellow. Top universities in the world prefer this book. I don’t think anyone has read deep learning but hasn’t read this book.
If you wanna buy this book,
Deep learning vs Neural Network
|Neural network||Deep learning|
|Definition||It is a model inspired by human brain||Deep learning is different from neural networks because of its hidden layer only.|
|Components||Neuron, connections and weights, propagation functions, and learning rate||motherboard, processors, RAM, physical memory, PSU|
|Architecture|| Feedforward neural network:- This architecture contains the first layer as the input layer, the last layer as the output layer, and all the middle layers as the hidden layer.|
Recurrent network:- This architecture is the opposite of a feedforward network. RNN has a backward connection between the hidden layers. They have some kind of memory in them.
Symmetrically connected network:- They are like a recurrent network. The only difference is that the connections between units are symmetrical(they have the same weight in both directions)
|Unsupervised pre-trained network:- As you might have guessed, this architecture is pretrained using past experiences. This includes autoencoders, generative adversarial network(GAN), and deep belief network.|
Convolutional neural network:- CNN are widely used for image recognition tasks.
Recurrent neural network:- In RNN, the output of the previous unit is fed to the input of the next unit.
Recursive neural network:- This architecture is mostly used to processing the sequence of words. It is used in NLP(natural language processing).
|Accuracy||less accuracy||more accuracy|
|Time taken||takes less time to train the model||takes more time to train the model|
As you have noticed above some parameters like accuracy, the time required to give the output. These parameters get differ in deep learning and neural network because of one thing i.e. number of hidden layers. It is the hidden layers that cause predict output with high accuracy in deep learning as compared to the neural networks.
How deep should my neural networks be?
First of all, I would say that there is no technique or general rules like how deep should your neural network be. It all depends on your problems, how big your dataset is, etc. If 3 layer neural network performs better for your problem, give output at the appropriate time. Then there is not any need to add more hidden layers.
I hope you liked the article.
If you have any question, please let me know in the comment box.
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