DEEP LEARNING FOR BEGINNERS: PART 2 - KEY DEEP LEARNING MODELS AND APPLICATION EXAMPLES

Deep Learning

In the first part of Deep Learning for Beginners haben wir die Grundlagen von Deep Learning erklärt. In diesem zweiten Teil werden wir uns einige der wichtigsten Deep-Learning-Modelle ansehen und zeigen, wie sie in der Praxis eingesetzt werden.

1. ARTIFICIAL NEURAL NETWORKS (ANNs)

ANNs are the foundation of Deep Learning. They consist of multiple layers of artificial neurons connected to each other. ANNs can learn to recognize complex patterns in data and learn from them.

Application examples:

  • Image recognition
  • Speech recognition
  • Machine translation
  • Text generation

2. CONVOLUTIONAL NEURAL NETWORKS (CNNs)

CNNs are a special type of ANNs developed for processing images and videos. CNNs can recognize spatial patterns in data, such as edges, shapes, and textures.

Application examples:

  • Image recognition
  • Object detection
  • Face recognition
  • Image segmentation

3. RECURRENT NEURAL NETWORKS (RNNs)

RNNs are another special type of ANNs developed for processing sequential data. RNNs can recognize temporal patterns in data, such as in texts, speech signals, and sensor data.

Application examples:

  • Speech recognition
  • Machine translation
  • Text generation
  • Time series analysis

4. GENERATIVE ADVERSARIAL NETWORKS (GANs)

GANs are a special type of Deep Learning models consisting of two neural networks: a generator and a discriminator. The generator generates new data while the discriminator tries to distinguish between real and fake data.

Application examples:

  • Image synthesis
  • Text generation
  • Music generation
  • Drug Discovery

5. Deep Reinforcement Learning (RL)

Deep RL is a branch of Deep Learning concerned with developing agents that can learn in an environment to solve a specific task. Deep RL agents can learn through trial and error to find the best possible action strategy.

Application examples:

  • Slot machines
  • Robotics
  • Financial markets
  • Autonomous vehicles

Conclusion

Deep Learning is a powerful tool that can be used for a variety of tasks. There are many different Deep Learning models, each suitable for specific tasks.

In this article, we introduced some of the most important Deep Learning models and showed how they are used in practice.