Deep learning architectures are the backbone of modern AI applications. They have revolutionized fields such as computer vision, natural language processing, and speech recognition. In this tutorial, we will explore some of the most popular deep learning architectures.

Convolutional Neural Networks (CNNs)

CNNs are primarily used for image recognition and classification tasks. They are designed to automatically and adaptively learn spatial hierarchies of features from input images.

  • LeNet-5: One of the earliest CNN architectures, known for its simplicity and effectiveness in handwritten digit recognition.
  • AlexNet: Introduced deep convolutional layers and batch normalization, significantly improving image classification performance.

LeNet-5 Architecture

Recurrent Neural Networks (RNNs)

RNNs are designed to handle sequential data, making them suitable for tasks like language modeling and time series analysis.

  • Elman Network: A simple RNN architecture that uses hidden units to store information about the previous state.
  • LSTM (Long Short-Term Memory): An improved version of RNN that can learn long-term dependencies and is less prone to vanishing gradient problem.

LSTM Architecture

Transformer

The Transformer architecture has become the standard for natural language processing tasks. It uses self-attention mechanisms to capture dependencies between words in a sequence.

  • BERT (Bidirectional Encoder Representations from Transformers): A pre-trained language representation model that can be fine-tuned for various NLP tasks.
  • GPT (Generative Pre-trained Transformer): A transformer-based model for natural language generation tasks.

Transformer Architecture

For more information on deep learning architectures, you can explore our Deep Learning Basics tutorial.