Deep learning is a subset of machine learning that focuses on artificial neural networks and their ability to simulate the human brain's architecture to process data and make decisions. It involves training artificial neural networks on large datasets to learn patterns and representations that can be used for various tasks such as classification, regression, image recognition, speech recognition, and natural language processing.
Key components of deep learning include:
- Neural Networks: At the core of deep learning are artificial neural networks, which are composed of layers of interconnected nodes (neurons). Each connection between nodes has an associated weight, and the network learns to adjust these weights during training to optimize its performance on a specific task.
- Deep Neural Networks (DNNs): Deep learning involves using deep neural networks, which have multiple layers (hence the term "deep"). These layers enable the network to automatically learn hierarchical representations of data, capturing complex features and patterns.
- Activation Functions: Activation functions introduce non-linearity to the neural network, allowing it to learn from more complex data. Common activation functions include sigmoid, tanh, and rectified linear unit (ReLU).
- Backpropagation: Backpropagation is the training algorithm used in deep learning. It involves adjusting the weights of the neural network based on the error (the difference between predicted and actual output) to minimize this error during training.
- Convolutional Neural Networks (CNNs): CNNs are a type of deep neural network designed for tasks involving grid-like data, such as images. They use convolutional layers to automatically and adaptively learn spatial hierarchies of features.
- Recurrent Neural Networks (RNNs): RNNs are suitable for tasks involving sequential data, such as time series or natural language processing. They have connections that form directed cycles, allowing them to maintain a memory of previous inputs.
- Transfer Learning: Transfer learning is a technique in deep learning where a model pre-trained on a large dataset is adapted for a new, related task with a smaller dataset. This approach leverages the knowledge gained during the pre-training phase.
- Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator, and a discriminator, trained simultaneously through adversarial training. GANs are used for generating new, realistic data, such as images or sound.
