Elixir vs Python in Neural Network Performance and Development Experience

The presentation by A. Neto & L. C. Tavano focused on benchmarking performance differences when training convolutional neural networks using the popular MNIST and CIFAR-10 datasets. They introduced Nx, the numerical Elixir library for tensor operations launched by José Valim and Sean Moriarity to facilitate GPU-intensive operations. The talk highlighted the key objectives, which were to educate about Nx library, compare Nx with Python's Keras in resource utilization and training time, and discuss the experiences of developing neural networks in both programming languages. The analysis demonstrated notable performance improvements in Elixir with the Nx library compared to its capabilities before Nx. However, Python with Keras still showed a performance lead, albeit with Elixir's performance now being within a competitive range. The talk showcased Elixir's evolving ecosystem and its potential for machine learning applications, indicating a promising future with further development and optimization.