Types of Neural Networks in Data Science

Types of Neural Networks in Data Science

1. Feedforward Neural Networks (FNNs)

• Structure: Data flows in one direction, from input to output.
• Use Case: Basic regression and classification tasks. 

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2. Convolutional Neural Networks (CNNs)

• Structure: Specialized for processing grid-like data, such as images.
• Key Features: Convolutional layers for feature extraction and pooling layers for dimensionality reduction.
• Use Case: Image recognition, object detection, medical imaging.

3. Recurrent Neural Networks (RNNs)

• Structure: Designed for sequential data with connections allowing information to persist over time.
• Key Features: Includes Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) for handling long-term dependencies.
• Use Case: Time series forecasting, natural language processing (NLP), speech recognition.

4. Autoencoders

• Structure: Encode input data into a compressed representation and then decode it.
• Use Case: Dimensionality reduction, anomaly detection.

5. Generative Adversarial Networks (GANs)

• Structure: Comprises two networks, a generator and a discriminator, competing against each other.
• Use Case: Generating realistic images, synthetic data creation. 

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6. Transformer Networks

• Structure: Uses attention mechanisms to weigh the importance of different data points.
• Use Case: NLP tasks like translation (e.g., BERT, GPT).