✅ Deep Learning Models You Should Know 🧠📚

1️⃣ Feedforward Neural Networks (FNN)
– Basic neural networks for structured/tabular data
– Example: Classification or regression on tabular datasets

2️⃣ Convolutional Neural Networks (CNN)
– Specialized for image and spatial data
– Example: Image classification, object detection

3️⃣ Recurrent Neural Networks (RNN)
– Processes sequential data
– Example: Time series forecasting, text generation

4️⃣ Long Short-Term Memory (LSTM)
– A type of RNN for long-range dependencies
– Example: Stock price prediction, language modeling

5️⃣ Gated Recurrent Unit (GRU)
– Lightweight alternative to LSTM
– Example: Real-time NLP applications

6️⃣ Autoencoders
– Unsupervised learning for feature extraction & denoising
– Example: Anomaly detection, noise reduction

7️⃣ Generative Adversarial Networks (GANs)
– Generates synthetic data by pitting two networks against each other
– Example: Deepfakes, art generation, image synthesis

8️⃣ Transformer Models
– State-of-the-art for NLP and beyond
– Example: Chatbots, translation (BERT, GPT)

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