7. Deep Learning & Neural Networks
Deep learning has fundamentally changed how computers process unstructured data through the use of artificial neural networks inspired by the human brain. Key principles include architectures like multi-layer perceptrons, convolutional neural networks, and recurrent neural networks. Various optimization methods and regularization techniques are critical for training these models effectively. The chapter also explores advanced frameworks that have made deep learning accessible across different domains, ranging from image processing to natural language processing and autonomous systems.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Deep learning is a subset of machine learning focusing on neural networks for complex data processing.
- Artificial neural networks consist of layers including input, hidden, and output layers which enable the learning process.
- Loss functions and optimization techniques such as backpropagation and gradient descent are crucial for training models effectively.
- Regularization techniques help prevent overfitting, ensuring models generalize well to unseen data.
Key Concepts
- -- Artificial Neural Networks (ANN)
- Computational models inspired by the human brain, used to recognize patterns in data.
- -- Activation Function
- Functions that determine the output of a neuron based on its input, introducing non-linearity into the model.
- -- Backpropagation
- A method used in training artificial neural networks, which computes the gradient of the loss function with respect to each weight by the chain rule.
- -- Regularization
- Techniques used to prevent overfitting by adding information or constraints to the model.
- -- Transfer Learning
- The practice of using pre-trained models on new problems, effectively reducing training time and resource requirements.
- -- Convolutional Neural Networks (CNN)
- A class of deep neural networks commonly used for analyzing visual data.
- -- Recurrent Neural Networks (RNN)
- Neural networks designed to recognize sequences, useful for tasks like time series or natural language processing.
Additional Learning Materials
Supplementary resources to enhance your learning experience.