Pytorch
How can we bring AI to life? Creating neural networks using PyTorch!
THE STORY
Through time there have been a variety of deep-learning frameworks: Theano, Torch, MXNet… the list goes on, but from all of them arose the big-two giants — TensorFlow and PyTorch. TensorFlow was once the all mighty, unanimously popular framework of choice, however now the era of PyTorch begins as more and more people move towards it.
PyTorch provides an ultra-flexible way to progressively code our network.
It’s simple, it’s robust and best of all, it’s elegant!
Creating and training neural networks with PyTorch happens like the general structure of any data science project:
Start with data — Creating dataset classes and defining transformations
Create models — Defining the architecture (i.e. layers, loss and optimiser used) and whether to use a pretrained network
Training — Stitching everything together and then evaluating it
We will be discussing this with a focus on images, but remember that these same techniques (often further refined) are used to process textual and video data!