8. Deep Learning and Neural Networks
Deep learning has significantly advanced the capabilities of machine learning by mimicking the brain's neural structure through artificial neural networks (ANNs), particularly deep neural networks (DNNs). By utilizing various architectures such as CNNs, RNNs, and GANs, deep learning enables remarkable performance in tasks ranging from image processing to natural language understanding. However, challenges such as overfitting, explainability, and computational demands require careful consideration for ethical and effective application.
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What we have learnt
- Deep learning is inspired by the structure and function of the human brain.
- Deep neural networks consist of multiple layers that learn complex features and representations.
- Activation functions, loss functions, and optimization techniques are critical for training effective neural networks.
- Various deep learning architectures are tailored for different types of data such as images, sequences, and unsupervised learning.
Key Concepts
- -- Artificial Neural Network (ANN)
- A computational model that consists of interconnected nodes (neurons) designed to simulate the way the human brain operates.
- -- Deep Neural Network (DNN)
- A neural network with multiple hidden layers that enhances its ability to learn complex representations from data.
- -- Activation Function
- A mathematical operation applied to a neuron's input that introduces non-linearity, essential for learning complex patterns.
- -- Backpropagation
- An algorithm used for training neural networks by calculating gradients of the loss function with respect to weights.
- -- Transfer Learning
- A technique in which a pre-trained model is reused and fine-tuned for a different but related task, saving time and resources.
- -- Regularization
- Techniques used to prevent overfitting by adding penalties to the loss function or modifying network architecture during training.
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