A naukri.com initiative
Medium
1M
146
Image Credit: Medium
Neural Networks: The Foundation of Modern Artificial Intelligence
- Neural networks are computational models mimicking human brain neurons, forming the backbone of modern AI and deep learning.
- They consist of layers of interconnected artificial neurons processing data through weighted connections, learning from experience.
- Neural networks operate through forward propagation and backpropagation, adjusting weights to enhance accuracy over time.
- The network comprises input, hidden, and output layers, each with specific functions in data processing and learning.
- Activation functions like sigmoid, ReLU, and softmax introduce non-linearity and aid in learning complex patterns.
- Training algorithms optimize network parameters based on input data, emphasizing the importance of high-quality datasets.
- Feedforward neural networks flow data from input to output without feedback loops, suitable for structured data tasks.
- Convolutional neural networks excel in image processing, utilizing convolutional, pooling, and fully connected layers for pattern recognition.
- Recurrent neural networks specialize in sequential data tasks, addressing time dependencies through recurrent connections.
- LSTMs and GRUs improve upon standard RNNs by handling long-term dependencies more effectively.
Read Full Article
8 Likes
For uninterrupted reading, download the app