Neural Network Performance Analysis

This chapter examines the performance of recurrent neural networks, particularly focusing on the JANET model compared to LSTMs across various datasets like MNIST and the MIT-BIH arrhythmia dataset. It delves into the importance of memory management, initialization techniques, and explores advanced methods such as pruning and quantization to enhance computational efficiency. Additionally, the discussion highlights future research directions, emphasizing the potential for simplifying neural network architectures to improve both accuracy and efficiency.