Artificial Neural Networks
Business leaders must stay informed about cutting-edge innovations in today's fast-paced technological landscape. Artificial Neural Networks (ANNs) are at the forefront of AI research and application.
We've curated this glossary tailored for executives to facilitate a better understanding. It provides clear definitions and insights into artificial neural networks, their applications, and their potential to transform various industries.
What are artificial neural networks?
Artificial Neural Networks (ANNs) are computational models inspired by the human brain's structure and function.
What are the key components of artificial neural networks?
The key elements that constitute artificial neural networks include:
- Input layer - The input layer receives data or features to be processed.
- Hidden layers - Hidden layers between the input and output layers process and transform data.
- Output layer - The output layer provides the network's final predictions or classifications.
- Neurons (nodes) - Neurons simulate biological neurons, performing calculations and transmitting signals.
- Weights - Weights represent connection strengths between neurons, adjusting during training.
- Activation functions - Activation functions introduce non-linearity into the network, enabling complex mappings.
- Bias - Bias terms allow fine-tuning of neuron activations.
How do ANNs learn from data?
Here are a few methods by which artificial neural networks learn from data:
- Supervised learning - ANNs learn from labelled data, mapping inputs to correct outputs.
- Unsupervised learning - ANNs identify patterns and relationships in unlabeled data.
- Backpropagation - Backpropagation is the iterative process of adjusting weights to minimise prediction errors.
- Epoch - An epoch represents one complete pass through the training data.
- Gradient descent - Gradient descent optimises weights to reduce prediction errors.
What are the different types of artificial neural networks?
An overview of various ANN architectures and their applications.
Feedforward neural networks (FNNs)
Recurrent neural networks (RNNs)
Convolutional neural networks (CNNs)
Generative adversarial networks (GANs)
Long short-term memory networks (LSTMs)
Self-organising maps (SOMs)
Data flows from input to output in one direction.
RNNs process sequential data, retaining memory of past inputs.
CNNs excel in image and spatial data analysis.
GNNs generate data by pitting two networks against each other.
LSTMs improve rnns' ability to handle long sequences.
SOMs perform unsupervised learning for clustering and visualisation.
What are the applications of artificial neural networks?
Exploring real-world applications powered by artificial neural networks.
- Image and speech recognition - ANNs power facial recognition, speech-to-text, and image classification.
- Natural language processing (NLP) - ANNs enable sentiment analysis, chatbots, and language translation.
- Autonomous vehicles - Neural networks process sensor data for self-driving cars.
- Financial forecasting - ANNs predict stock prices and detect fraudulent transactions.
- Healthcare - ANNs assist in disease diagnosis and drug discovery.
What are the advantages of artificial neural networks?
Strengths of ANNs in data analysis and automation
- Image and speech recognition - ANNs power facial recognition, speech-to-text, and image classification.
- Pattern recognition - ANNs excel at recognising complex patterns in data.
- Adaptability - They adapt to new data and changing conditions.
- Parallel processing - ANNs perform parallel computations, speeding up tasks.
- Automation - ANNs automate tasks like image classification and data analysis.
