Deep Learning and Neural Networks
Deep Learning is a specialized branch of Machine Learning that uses multi-layered neural networks to model complex patterns in data. It is particularly useful in processing large-scale datasets and performing high-dimensional feature extraction.
Components of Neural Networks:
- Input Layer – Receives raw data.
- Hidden Layers – Contains neurons that transform input data through activation functions.
- Output Layer – Provides the final result or classification.
Popular Neural Network Architectures:
- Convolutional Neural Networks (CNNs) – Used in image recognition and computer vision.
- Recurrent Neural Networks (RNNs) – Effective for sequential data, such as speech and text processing.
- Transformer Networks – Advanced models like BERT and GPT, widely used in Natural Language Processing (NLP).
Frameworks such as TensorFlow and PyTorch provide tools for developing and training deep learning models efficiently.
