Chapter
The Challenges of NumPy and the Future of Multi-dimensional Arrays
1:27:30 - 1:33:27 (05:56)
The guest explores the challenges faced by NumPy development and its funding, as well as the future of multi-dimensional arrays and how they could potentially operate over smarter systems in the future.
Clips
The split in the array community between TensorFlow and PyTorch highlights the need for NumPy to efficiently operate over different types of multidimensional arrays.
1:27:30 - 1:29:46 (02:15)
Machine Learning
Summary
The split in the array community between TensorFlow and PyTorch highlights the need for NumPy to efficiently operate over different types of multidimensional arrays.
ChapterThe Challenges of NumPy and the Future of Multi-dimensional Arrays
Episode#224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming
PodcastLex Fridman Podcast
The compatibility of PyTorch and TensorFlow has become increasingly difficult to manage, and the future of NumPy development.
1:29:46 - 1:33:27 (03:40)
TensorFlow and PyTorch
Summary
The compatibility of PyTorch and TensorFlow has become increasingly difficult to manage, and the future of NumPy development. It is necessary to balance the needs of hackers with big corporate clients, but this poses the challenge of whether this is hampering the progress of open-source development.