Deep learning is a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn from large amounts of data. Here are some key concepts and resources to help you understand the fundamentals of deep learning.
Key Concepts
- Neural Networks: Deep learning is based on neural networks, which are inspired by the human brain's structure and function.
- Layers: Neural networks consist of layers, including input, hidden, and output layers.
- Activation Functions: These functions help determine whether a neuron should be activated or not.
- Backpropagation: This is a technique used to train neural networks by adjusting the weights and biases based on the error rate.
Resources
- Introduction to Deep Learning - A comprehensive guide to getting started with deep learning.
- Neural Networks Explained - A detailed explanation of how neural networks work.
Neural Network Diagram
Further Reading
- Deep Learning with Python - A book that provides a comprehensive introduction to deep learning using Python.
- TensorFlow - An open-source library for machine learning developed by Google Brain.
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