Deep Learning Foundations and Applications

Definition and core idea of deep learning

Deep learning is a branch of machine learning that uses multilayered neural networks to learn from data and perform tasks such as classification, regression, and representation learning. It is inspired by biological neuroscience, using stacked “artificial neurons” arranged in layers and trained to transform inputs into useful internal representations. The core idea is that “deep” refers to the number of layers (often from a few to hundreds or thousands) through which data is transformed. As an example in image recognition, early layers can learn simple features (like edges), while later layers build more complex concepts (like faces). Unlike earlier approaches that relied heavily on hand-crafted feature engineering, deep learning can automatically discover which features to extract and at what level, using supervised, semi-supervised, or unsupervised training methods.

0/2 modes complete

Layer hierarchy and feature representation learning

0/2 modes complete