Overview of Neural Networks
Do you wanna know basically what is a neural network?
Artificial Intelligence is so popular in our world. If you are interested in AI, You would probably know the existence of an Artificial Neural Network. Do you wanna know basically what is a neural network? Then come and dive into NeuralNets by this article.
Table of Contents
What is Neural Network?
Which areas do we use NNs?
How is work a Neural Network?
Shallow Neural Network
Deep Neural Network
What is Neural Network?
Neural Network, is just simulated and inspired by real neurons. It is a Supervised System made up of neurons and layers. Every neuron connected with the before layer’s neurons through this Neural Network can learn and solve complex problems. Neural Networks have this ability; Learn and Solve.
Which areas do we use ANNs?
Companies such as Visa, Master Cards, American Express, Europen Banks, USA Banks, ASIA Pacific Institution, High Street Banks, and Brokers in the UK have hugely invested in neural network technology. An article in The Economist magazine proved with numerical data that sales and internal volume including neural network technology have been on the rise in recent years.
Neural Networks can be applied in a lot of areas such as Finance, Engineering, Medicine, Manufacture. Neural Network can detect damage possibilities for a product before happened. Also, NN’s Prediction abilities proved in finance areas. Neural Networks can work effectively in areas such as macroeconomic predicting, credit scoring, currency forecasting, and risk analysis.
Finance: Fraud Detection, Credit Scoring
Computer Vision: Face-Motion-Object-Emotion Recognitions
Bioinformatics: Tumor Detection, Cancer Detection, Stroke Detection
Energy Production: Price and Load Forecast
Automotive, Aerospace: Predictive Maintenance
Natural Language Processing: Voice Assistant, Machine Translation, Speech Recognition, Chatbots …
How is work a Neural Network?
Neural Network, is mathematical systems composed of a lot of connected neurons. Artificial neurons are connect with each other, it is like Biological Neurons. Just like them, they receive input signals, collect these signals, process them, and deliver the outputs.
Neural Network have five sections:
Inputs: Its inputs that NN take first
Weights: Our weight parameter is our NN most important parameter
Addition: Inputs and weights multiplied and we add our bias parameter.
Activation Function: We have to know our neuron’s activity()
Outputs: Output of the activation func. is our neuron’s output. Every neuron would have a lot of input but output must 1 value.
Scale of Neural Networks:
Just have two parts Shallow and Deep Neural Networks.
Shallow Neural Networks:
“Shallow” have just 3 layer for neurons.
- Input layer
- Hidden layer (Math processes is happening here)
- Output layer (Our statistical result)
In brief, “shallow” neural networks generally using for basic problems. Because with just 1 hidden layer you can’t extract specific features.
There are articles express deep neural networks have give better result than shallow neural networks. We can say that the hidden layers is effective way for feature extraction.
Deep Neural Networks:
It is the developed form of neural networks and its hidden layers are more than one. Ian Goodfellow, Bengio and Courville are proved neural networks would solve complex problem but deep neural network’s have better accuracy, because when you add more layer you would have better accuracy. Hidden layers are useful untill 9–10 layers, after your prediction will worse. Nowadays, most neural network models and applications have deep neural network with 3–10 layers.
Gradient descent can write code better than you. I’m sorry. -Andrej Karpathy
There are a lot of deep learning architecture. These architectures can integrate a lot of areas and have good results.
CNN(Convolutional Neural Network), RNN(Recurrent Neural Network), GAN(Generative Adversarial Network), LSTM(Long-Short-Term-Memory) is some type of advanced level architectures. GoogleNet, VGG-16, AlexNet, LeNet, U-Net some of deep learning models.
Resources:
https://teknikafa.com/yapay-sinir-aglari/
https://missinglink.ai/guides/neural-network-concepts/complete-guide-artificial-neural-networks/
https://www.digitaltrends.com/cool-tech/what-is-an-artificial-neural-network/
https://deepai.org/machine-learning-glossary-and-terms/neural-network
