End-to-End Deep Learning

End-to-End Deep Learning refers to training a single neural network to handle an entire task directly, from input to output, without breaking it into smaller steps. For example, in image recognition, the system learns to go straight from an image to identifying what's in it. It automatically learns the best features and representations needed, reducing the need for manual feature extraction. This approach simplifies the workflow, often improves accuracy, and allows the model to optimize the full process as a whole, making it powerful for complex tasks like speech translation, autonomous driving, and more.