We have entered the Big Data Era, where the explosion of data across diverse domains — from science and engineering to healthcare, finance, and society at large — has created unprecedented challenges and opportunities. A central issue lies in how to harness this massive scale of data to derive meaningful insights and enable intelligent services. In this context, Deep Learning has emerged as a transformative paradigm, capable of capturing complex patterns, representations, and predictive structures directly from raw data. Building on advances in neural network architectures — including Convolutional Networks, Transformers, and Large-Scale Pretrained Models — Deep Learning enables breakthroughs in areas such as computer vision, natural language processing, generative modeling, bioinformatics, and graph-based reasoning. This course delves into core and advanced deep learning techniques, exploring their theoretical foundations, practical implementations, and real-world applications in tackling large-scale and high-dimensional problems.
