diff options
| author | Daksh Miglani <hello@dak.sh> | 2017-07-14 03:44:05 +0300 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2017-07-14 03:44:05 +0300 |
| commit | ca6de57dae2a01ed7b9e5fba28ffd0f2a7861984 (patch) | |
| tree | 9b0b1d2580144d419f93ec61c171bc19e62b6085 | |
| parent | 37e01fbedcc5ec5ccc5be56f4f417dccbb13ca76 (diff) | |
Heading Fixes
Homepage heading fixes :)
| -rw-r--r-- | index.md | 6 |
1 files changed, 3 insertions, 3 deletions
@@ -5,7 +5,7 @@ id: home hero: true --- -##What is Torch? +## 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, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. @@ -22,14 +22,14 @@ A summary of core features: * Embeddable, with ports to iOS and Android backends -##Why Torch? +## 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 <strong><a href="https://github.com/torch/torch7/wiki/Cheatsheet">large ecosystem of community-driven packages</a></strong> 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 efficient manner. -##Using Torch +## Using Torch Start with our <a href="/docs/getting-started.html">Getting Started</a> guide to download and try Torch yourself. Torch is open-source, so you can also start with the code on the <a href="https://github.com/torch/torch7">GitHub repo</a>. |