WHAT IS TORCH?

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, because of the easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

KEY FEATURES:

  • A powerful N-dimensional array
  • Various routines for indexing, slicing, and transposing
  • Amazing interface to C, via LuaJIT
  • Linear algebra routines
  • Neural network and energy-based models
  • Numeric optimization routines
  • Embeddable, with ports to iOS, Android and FPGA backends

WHO IS TORCH FOR?

Torch has its origins in academia and eventually developed a large open source user base. It has a large amount of user support, blogs, and supporting documents across the internet and Academic Literature. If you are using an Ubuntu platform, Torch is probably the easiest to get up and running.

WHY TORCH?

The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community.

At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an effcient manner.

EXXACT TORCH SUPPORTED SOLUTIONS

Exxact Deep Learning GPU Solutions are powered by the leading hardware, software, and system engineering. With pre-installed Torch, as well as other leading deep machine learning software packages, Exxact Deep Learning GPU Solutions are fully turn-key and designed for rapid development and deployment of optimized deep neural networks with multiple GPUs.


TORCH EXAMPLES AND APPLICATIONS

Convolutional Network, for Natural Images:

Logistic Regression:

Optimized Differently:

Generating Faces with Torch:
Implement a generative image model that converts random noise into images of faces: