This episode breaks down the 'Neural Turing Machines' paper, which proposes a new neural network architecture called the Neural Turing Machine (NTM), which combines the power of traditional neural networks with an external memory component that can be addressed and manipulated through attentional processes. The NTM aims to bridge the gap between modern machine learning and the fundamental mechanisms of computation found in conventional computers, such as external memory access and logical flow control. The paper explores the NTM’s ability to learn and execute simple algorithms like copying, sorting, and associative recall, demonstrating its potential for learning complex programs and surpassing the limitations of traditional recurrent neural networks (RNNs) in handling long-term dependencies and variable-length structures.

Audio : (Spotify) https://open.spotify.com/episode/2rZ05v62e2FUFa0p4OVsTe?si=GMa0Q6jiSziEQocZbV4OhQ

Paper: https://arxiv.org/abs/1410.5401