Deep Learning vs Neural Network, The Main Differences!

Do you wanna know about Deep Learning vs Neural Network, the main differences between Deep Learning and Machine Learning?. If yes, then give your few minutes to this article and read it till the end. Here I will discuss the Deep Learning vs Neural Network.

Hello, & Welcome!

In this blog, I am gonna tell you-

1. Deep Learning vs Neural Network.

So let’s get started-

Deep Learning vs Neural Network

To understand the difference between Deep Learning and Neural Network. First, you should know its definition.

So, let’s start with Deep Learning.

Deep Learning-

Deep Learning is the subpart of machine learning. The objective of Deep learning is to make machines as smart as humans. Deep learning is much powerful than machine learning.

Machine learning can work on a small amount of data. But if you have a large dataset, then machine learning fails. And here deep learning come into the picture.

Deep Learning can easily perform the operation on a huge amount of data. This functionality makes deep learning more robust than machine learning.

In deep learning, machines can learn itself. They don’t require any supervision.

Neural Network-

You can say the neural network an artificial human brain. Yes, it’s right. The structure of the neural network is much similar to the human brain.

Neural network comprise of neurons. The artificial neurons. In the neural network, each neuron is connected with other neurons.

To understand the neural network, first, see how human neuron looks.

Human Neuron-

This is a human neuron. Where you can see dendrites, axons, and neuron. But single neuron is useless, just like an ant. A single ant is useless, but thousand of ants can build a colony. Similarly, thousands of neurons build a full brain.

In the same way, the artificial neural network works. Artificial neural networks basically have three layers. The input layer, the hidden layer, and the output layer.

You can understand the structure of the neural network in the below image.

Artificial Neural Network-

All the circles you are seeing in the image are neurons. And one neuron is connected with another neuron with synapses. Synapses are nothing but the connecting lines. Different weights are assigned to these synapses.

When a neural network predicts some output, so this predicted output is matched with actual output. This matching is done with the help of cost function. The formula of the cost function is-

cost function= 1/2 square(y – y^)

After calculating the cost function, the weights are updated. This weight update process is performed with the help of backpropagation.

In neural networks, there may be more than one hidden layer. The deeper the hidden layers, the more accurate the neural network is.

The main differences between Deep Learning vs Neural Network-

Let’s focus on some key differences between the Deep Learning and Neural Network.

There are various misconceptions for Deep Learning vs Neural Network. So, I will discuss the difference between deep learning and neural network with the help of misconceptions you have.

So, the very first misconception most of us have is-

1. Deep Learning == CNNs and RNNs.

That is wrong. CNN and RNN are part of the neural network. Their names suggest that they are part of a neural network. Because of Convolutional Neural Network and Recurrent Neural network. It’s not Convolutional Deep Learning and Recurrent Deep Learning. Right!

So studying CNN means you are studying neural networks. But simultaneously you are studying deep learning too.

2. History

The RNN and CNN were invented in the 90s. But in the 90s deep learning didn’t exist. That means CNN and RNN were older concepts than deep learning.

3. Neural Networks are Superset of Deep Learning.

First look at the image.

After looking at this image. What you understand?.

The neural network is a superset of deep learning. That means all deep learning is neural networks, but all neural network is not deep learning.

4. Components-

The neural network has the following components-

1. Neurons- Neuron takes input from the previous layer, perform certain operations, and produce the output.
2. Synapses- These are the connecting lines. Synapses connect one neuron to another neuron.
3. Weights- The weights are assigned to each synapse. Weights are the only thing on which neural network has control. You can adjust the weight of neural network.

Deep Learning has the following components-

1. Motherboard- The motherboard is a component of deep learning.
2. Processors- Deep learning requires GPU instead of CPU. Because deep learning works on huge data, that’s why to process huge data, deep learning required GPU.
3. PSU- Deep learning process huge data, therefore it requires a huge memory. It also required a large PSU to handle huge power.

5. Architecture-

The neural network has the following architecture-

1. Feed-Forward Neural Network- In a feed-forward neural network, pass flow from one direction. From the input layer to the hidden layer, and from the hidden layer to the output layer.
2. Symmetrically Connected Networks- It is much more likely to the Recurrent Network. Symmetrically connected networks that have hidden layers are known as Boltzmann machines.

The deep learning has following architecture-

1. Convolutional Neural Network- CNN is used for image recognition. CNN converts the image into pixel values. These pixel values are features of an image. These features are sent into an input layer.
2. Recurrent Neural Network- In RNN, the output of the previous layer is the input of the current layer. RNN process the sequentially stored data.

So that’s all about Deep Learning vs Neural Network. I hope now you understand the difference between Deep Learning and Neural Network. If you have any questions, feel free to ask me in the comment section.

Enjoy Learning!

All the Best!

For more details on Deep Learning, Read this article- What is Deep Learning and Why it is Popular?

If you wanna know about the neural network learning process? read it from here.

Wanna learn Artificial Neural Network? If yes, read it here.

Thank YOU!

Though of the Day…

‘ It’s what you learn after you know it all that counts.’

– John Wooden

Read Deep Learning Basics Here