1. Recurrent Neural Networks (RNN)
- Processes sequential data by maintaining a "memory" of previous inputs
- Good for time series, text, speech processing
- Has variants like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) that handle long-term dependencies better

2. Convolutional Neural Networks (CNN)
- Specialized for processing grid-like data (especially images)
- Uses convolution operations to detect patterns and features
- Common in computer vision tasks

3. Feedforward Neural Networks (FNN)/Multi-Layer Perceptrons (MLP)
- The most basic type, data flows only forward through layers
- Good for simple classification and regression tasks
- No memory or feedback connections

4. Transformers
- Modern architecture that uses attention mechanisms
- Excellent at processing sequences while handling long-range dependencies
- Powers models like BERT, GPT, and many modern language models

5. Autoencoders
- Learn to compress data into a lower-dimensional representation
- Useful for dimensionality reduction and feature learning
- Can be used for anomaly detection and generative tasks

6. Generative Adversarial Networks (GANs)
- Two networks compete: one generates fake data, one detects fakes
- Used for generating realistic images, videos, and other content
- Popular in creative and artistic applications



dive deeper into each architecture:

1. Recurrent Neural Networks (RNN) in detail:
- Think of RNNs as networks with a memory loop
- At each step, they consider both current input and previous state
- Basic RNN formula: ht = tanh(Whh * ht-1 + Wxh * xt + bh)
- Challenges: Vanishing/exploding gradients over long sequences
- LSTM solves this with gates:
  - Forget gate: decides what to remove from memory
  - Input gate: decides what new information to store
  - Output gate: decides what parts of memory to output
- GRU is a simpler variant with just reset and update gates

2. Convolutional Neural Networks (CNN) in detail:
- Uses sliding windows (kernels) to process data
- Key layers:
  - Convolutional layers: detect features using filters
  - Pooling layers: reduce dimensionality (max or average pooling)
  - Fully connected layers: final classification/regression
- Features hierarchical learning:
  - Early layers: basic features (edges, colors)
  - Middle layers: textures, patterns
  - Deep layers: complex objects, concepts

3. Transformers in detail:
- Revolutionary architecture using self-attention
- Key components:
  - Multi-head attention: processes relationships between all inputs
  - Positional encoding: adds position information
  - Feed-forward networks: processes transformed representations
- Encoder-decoder structure:
  - Encoder: processes input sequence
  - Decoder: generates output sequence
- No recurrence needed, enabling parallel processing

4. GANs in detail:
- Two competing networks:
  - Generator: creates fake data
  - Discriminator: tries to spot fakes
- Training process:
  - Generator aims to fool discriminator
  - Discriminator aims to correctly classify real/fake
  - Results in increasingly realistic generations
- Variants:
  - DCGAN: Deep Convolutional GAN
  - CycleGAN: unpaired image translation
  - StyleGAN: high-quality image generation

5. Autoencoders in detail:
- Structure:
  - Encoder: compresses input to latent space
  - Decoder: reconstructs input from latent space
- Types:
  - Vanilla: basic compression/reconstruction
  - Variational (VAE): adds probabilistic encoding
  - Denoising: learns to remove noise
- Applications:
  - Data compression
  - Feature learning
  - Anomaly detection
  - Image generation