Mathematical Foundations of Deep Learning: Driving AI Evolution [Video]

Deep learning has advanced significantly over the past decade, largely due to its strong mathematical foundations.  Chandrasekhar Karnam explores how linear algebra, calculus, and probability theory have driven this evolution, enabling the creation of powerful neural network architectures. These mathematical principles have been key in shaping deep learning’s capabilities and continue to offer valuable insights into the future of artificial intelligence.

Linear Algebra: The Backbone of Neural Networks

Linear algebra forms the core of deep learning, facilitating the representation and manipulation of data within neural networks. Vectors and matrices serve as the building blocks, allowing efficient data transformations. For instance, operations like matrix multiplication and convolution underpin architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Recent advancements in hardware, such as GPUs, have accelerated these matrix operations, enabling the training of models with billions of parameters. Techniques like Singular Value Decomposition (SVD) and tensor decomposition further optimize neural networks, enhancing …