Abstract

Deep learning is a subfield of machine learning that has achieved remarkable success across a wide range of domains, including computer vision, speech recognition, natural language processing, bioinformatics, and more. In this foundational paper, LeCun, Bengio, and Hinton provide a comprehensive overview of deep learning—its principles, architecture, training methods, and transformative applications. At its core, deep learning refers to computational models composed of multiple layers of non-linear processing units that learn to represent data with increasing levels of abstraction. Unlike traditional machine learning approaches that require handcrafted features designed by domain experts, deep learning models can automatically learn useful representations from raw data. This capability has enabled breakthroughs in tasks that were previously considered difficult or impossible for machines. The authors discuss representation learning—the process by which a model learns to transform raw input (like pixels or audio waveforms) into a more useful internal representation. Deep learning models, especially multilayer neural networks, perform this through a hierarchy of feature extraction layers. Each layer captures more abstract patterns, enabling the model to build a rich internal understanding of complex data such as images, speech, or text. Supervised learning is the most widely used paradigm in deep learning, where the model is trained using labeled data. During training, the model predicts outputs and adjusts its internal weights based on the difference between its predictions and the correct answers. This adjustment is done using the backpropagation algorithm, which efficiently computes the gradient of the loss function with respect to each weight in the network. The paper emphasizes the impact of convolutional neural networks (CNNs) in visual tasks. CNNs leverage spatial hierarchies in image data using local connections, shared weights, and pooling layers, which greatly reduce the number of parameters and enhance generalization. CNNs have become the standard architecture in computer vision, powering systems for facial recognition, medical imaging, autonomous driving, and more. Recurrent neural networks (RNNs) are highlighted for their ability to handle sequential data, such as text and speech. With their internal memory, RNNs capture temporal dependencies, enabling applications in machine translation, sentiment analysis, and speech synthesis. Variants like LSTM networks improve the learning of long-term dependencies and have been widely adopted in modern NLP systems. The authors also note the role of large datasets, GPU acceleration, and improved training algorithms in the resurgence and success of deep learning. Furthermore, deep learning’s adaptability to various data modalities makes it a general-purpose approach applicable across many scientific and industrial domains. In conclusion, the paper underscores deep learning’s unique ability to scale with data and computation, automatically discover meaningful features, and solve complex real-world problems. As research advances, deep learning is expected to play a central role in the development of more intelligent, perceptive, and autonomous systems.