Deep learning is a subset of machine learning that focuses on neural networks with multiple layers (deep neural networks). It has led to significant advancements in various domains, including computer vision, natural language processing, and reinforcement learning.
Neural Network Architectures
Deep learning encompasses various neural network architectures, including:
- Convolutional Neural Networks (CNNs) for image processing.
- Recurrent Neural Networks (RNNs) for sequential data processing.
- Generative Adversarial Networks (GANs) for generating realistic data.
- Transformer architectures for natural language processing.
Training Deep Neural Networks
Training deep neural networks involves techniques such as stochastic gradient descent (SGD), mini-batch gradient descent, backpropagation, and optimization algorithms like Adam and RMSprop.
Transfer Learning
Transfer learning is a technique where pre-trained deep learning models are fine-tuned on new tasks or datasets, leveraging knowledge learned from previous tasks. It is widely used to improve model performance with limited labelled data.
Applications of Deep Learning
Deep learning has applications in various domains, including:
- Computer vision: Object detection, image classification, image segmentation.
- Natural language processing: Machine translation, text generation, sentiment analysis.
- Reinforcement learning: Game playing, robotics, autonomous vehicles.
- Healthcare: Medical image analysis, disease diagnosis, drug discovery.
- Finance: Algorithmic trading, fraud detection, risk assessment.