Not every regression or classification problem needs to be solved with deep learning. For that matter, not every regression or classification problem needs to be solved with machine learning. After all, many data sets can be modeled analytically or with simple statistical procedures.
On the other hand, there are cases where deep learning or deep transfer learning can help you train a model that is more accurate than you could create any other way. For these cases, PyTorch and TensorFlow can be quite effective, especially if there is already a trained model similar to what you need in the framework’s model library.
PyTorch builds on the older Torch and Caffe2 frameworks. As you might guess from the name, PyTorch uses Python as its scripting language, and uses an evolved Torch C/CUDA back-end. The production features of Caffe2 are being incorporated into the PyTorch project.
PyTorch is billed as “Tensors and dynamic neural networks in Python with strong GPU acceleration.” What does that mean?
Credit: Google News