Deep learning is a subset of machine learning that involves artificial neural networks with multiple layers. These layers enable the model to learn hierarchical representations of data, making it powerful for tasks like image recognition, natural language processing, and more.

Key Concepts

  • Neural Networks: Composed of layers (input, hidden, output) that process data through weighted connections.
  • Activation Functions: Non-linear functions like ReLU, Sigmoid, or Tanh that introduce complexity to models.
  • Training Process: Involves forward propagation, loss calculation, and backpropagation to adjust weights.
  • Optimization Algorithms: Techniques such as Gradient Descent or Adam minimize the loss function.

Applications

  • Computer Vision: Object detection, image classification (e.g., CNN Tutorial).
  • Natural Language Processing: Sentiment analysis, machine translation.
  • Reinforcement Learning: Game playing, robotics.

Learning Resources

For deeper exploration:

  1. Understanding Neural Networks
  2. Deep Learning Frameworks
  3. Hands-on Projects
neural_network_structure
backpropagation

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