How Do Artificial Neural Networks Work?

Artificial Neural Networks (ANN) are machine learning models inspired by the neurons in the human brain. These structures, which form the foundation of deep learning, enable multi-layered learning over complex data.

2. What is an Artificial Neural Network?

An artificial neural network is a model composed of an input layer, one or more hidden layers, and an output layer, with neurons in each layer connected to those in the next.

• Neuron (Node): Processes input data and produces an output.
• Weight: Indicates the importance of the connection between neurons.
• Bias: Used to shift the output.
• Activation Function: Used to calculate the neuron's output. E.g., ReLU, Sigmoid, Tanh

3. How Do Neural Networks Learn?

Forward Propagation

Input data is processed through the network using weights and activation functions and transformed into output.

Loss Function

The difference between the predicted output and the actual output is calculated (loss). E.g., MSE, Cross-Entropy

Backpropagation

Based on the calculated loss, weights are updated using backpropagation. The gradient descent algorithm plays a key role in this process.

4. Neural Network Architectures

• MLP (Multilayer Perceptron): Fully connected, basic architecture.
• CNN (Convolutional Neural Network): Focused on image processing.
• RNN (Recurrent Neural Network): Designed for time series and sequential data.
• LSTM / GRU: Advanced RNN types that can learn long-term dependencies.
• Transformer: A revolution in NLP, utilizing attention mechanisms.

5. Application Areas

• Image Recognition: Facial recognition, autonomous vehicle perception
• Natural Language Processing: Chatbots, automated translation
• Healthcare: Analysis of radiology images
• Finance: Stock price prediction
• Gaming and Robotics: Decision-making systems

6. A Simple Example

A simple neural network model using Python + Keras:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
model = Sequential([
Dense(16, activation='relu', input_shape=(10,)),
Dense(8, activation='relu'),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

7. Advantages and Challenges

Advantages:

• Can learn complex patterns
• Works with a wide variety of data types

Challenges:

• Requires a large amount of data and computational power
• Lack of interpretability (black-box issue)
• Risk of overfitting

8. Future Perspective

Neural networks are evolving into more flexible and interpretable versions integrated with large models like GPT, IoT, and robotic systems.

• Gürkan Türkaslan
• 7 January 2022, 10:14:42