Deep Learning Architecture Cheatsheet 🧠

A quick-start guide and example collection for five major deep learning architectures.

Architecture	Best For	Example Use Case
CNN (Convolutional Neural Network)	Image & video processing	Image classification, object detection
RNN / LSTM / GRU	Sequential data	Language modeling, speech recognition
Transformer	Long-range dependencies in data	Chatbots, translation, summarization
GAN (Generative Adversarial Network)	Generative tasks	Creating realistic images, deepfake videos
Autoencoder	Dimensionality reduction & denoising	Data compression, anomaly detection

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📂 Folder Guide

• cnn/ → Vision-based models (image classification, detection)
• rnn_lstm_gru/ → Sequential models for text, speech, and time series
• transformer/ → Attention-based models for language and more
• gan/ → Generative models for images and videos
• autoencoder/ → Compression, anomaly detection, and denoising

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🚀 Getting Started

1. Clone the repo

   git clone https://github.com/<your-username>/deep-learning-architecture-cheatsheet.git
   
   

deep-learning-architecture-cheatsheet/ ├── README.md ├── cnn/ │ ├── README.md │ ├── example_image_classification.ipynb │ └── example_object_detection.ipynb ├── rnn_lstm_gru/ │ ├── README.md │ ├── example_language_model.ipynb │ └── example_speech_recognition.ipynb ├── transformer/ │ ├── README.md │ ├── example_chatbot.ipynb │ └── example_translation.ipynb ├── gan/ │ ├── README.md │ ├── example_image_generation.ipynb │ └── example_deepfake_video.ipynb └── autoencoder/ ├── README.md ├── example_dim_reduction.ipynb └── example_anomaly_detection.ipynb