spaCy excels at large-scale information extraction tasks. It’s written from the ground up in carefully memory-managed Cython. Independent research has confirmed that spaCy is the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using.
Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling. It was designed with these key principles:
Universal: Pyro can represent any computable probability distribution.
Scalable: Pyro scales to large data sets with little overhead.
Minimal: Pyro is implemented with a small core of powerful, composable abstractions.
Flexible: Pyro aims for automation when you want it, control when you need it.
In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years.
This Blogpost Will Cover:
- Part 1: PyTorch Installation
- Part 2: Matrices and Linear Algebra in PyTorch
- Part 3: Building a Feedforward Network (starting with a familiar one)
- Part 4: The State of PyTorch
Torch is one of the most popular Deep Learning frameworks in the world, dominating much of the research community for the past few years (only recently being rivaled by major Google sponsored frameworks Tensorflow and Keras). Perhaps its only drawback to new users has been the fact that it requires one to know Lua, a language that used to be very uncommon in the Machine Learning community. Even today, this barrier to entry can seem a bit much for many new to the field, who are already in the midst of learning a tremendous amount, much less a completely new programming language.
However, thanks to the wonderful and billiant Hugh Perkins, Torch recently got a new face, PyTorch… and it’s much more accessible to the python hacker turned Deep Learning Extraordinare than it’s Luariffic cousin. I have a passion for tools that make Deep Learning accessible, and so I’d like to lay out a short “Unofficial Startup Guide” for those of you interested in taking it for a spin.