Deep learning is a subset of machine learning that focuses on algorithms inspired by the structure and function of the human brain, called neural networks. These networks learn patterns from data through multiple layers of processing, enabling complex tasks like image recognition and natural language understanding.

🔍 Key Concepts

  • Neurons: Basic units of neural networks that process and transmit data.
  • Layers: Stacks of neurons where input data is transformed through successive stages.
  • Activation Functions: Mathematical functions that determine the output of a neuron given an input or set of inputs.
  • Backpropagation: Algorithm used to train neural networks by adjusting weights based on error rates.

📊 Applications of Deep Learning

  • Computer Vision: Identifying objects in images (e.g., CNN Examples)
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  • Natural Language Processing (NLP): Understanding and generating human language
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  • Reinforcement Learning: Training models through trial and error (e.g., RL Basics)
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📘 Further Reading

For a deeper dive into machine learning fundamentals, check out our Machine Learning Tutorial.

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