🧠 Neural Networks
Computer structures that mimic human brain neurons for intelligent learning and decision-making
What are Neural Networks?
Neural Networks are mathematical models that mimic human brain neurons, consisting of interconnected nodes or neurons through weights, capable of learning patterns from data
Main Components
- Input Layer
- Hidden Layers
- Output Layer
- Weights & Biases
Working Process
Types of Neural Networks
Feedforward Networks
Basic networks where data flows forward only, no loops
- • Multi-layer Perceptron
- • Classification tasks
- • Regression problems
Recurrent Networks (RNN)
Networks with feedback loops, ideal for sequential data
- • LSTM, GRU
- • Time Series Analysis
- • Natural Language Processing
Convolutional Networks (CNN)
Specialized networks for image processing and Computer Vision
- • Image Recognition
- • Object Detection
- • Feature Extraction
Autoencoders
Networks that learn to compress and reconstruct data
- • Data Compression
- • Anomaly Detection
- • Feature Learning
Generative Networks (GANs)
Two competing networks that generate new data
- • Image Generation
- • Data Augmentation
- • Style Transfer
Transformer Networks
Modern networks using attention mechanisms
- • Self-Attention
- • Language Models
- • Vision Transformers
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