Deep Learning

Deep learning refers to machine learning using neural networks with multiple hidden layers — hence "deep." These deep architectures can learn hierarchical representations of data, automatically discovering features that would be difficult or impossible to engineer by hand.

Deep learning has driven breakthroughs in image recognition, speech processing, natural language understanding, game playing, and scientific discovery. Key architectures include convolutional neural networks (CNNs) for vision, transformers for language, and graph neural networks for structured data.

Deep learning engineers design, train, and optimize these models. The role requires proficiency in frameworks like PyTorch and TensorFlow, understanding of GPU computing, and knowledge of training techniques (learning rate scheduling, batch normalization, dropout, data augmentation). Strong mathematical foundations in linear algebra, calculus, and probability are also essential.