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The OCaml compiler team at OCamlPro is happy to present some of the work recently done jointly with JaneStreet's team. A lot of work has been done towards a new framework for optimizations in the compiler, called Flambda2, aiming at solving the shortcomings that became apparent in the Flambda optimi...
Jane Street’s intern program yet again is coming to an end, which is anice opportunity to look back over the summer and see what they’veaccomplished.
RFC 1951 is one of the most used standards. Indeed, when you launch your Linux kernel, it inflates itself according zlib standard, a…
Back when the Raspberry Pi was first released in 2012 Michael Bacarella wrotea blog poston using OCaml and Async on this little device.Since then installing ...
As our Tools & Compilers team has grown, the kinds of projects we workon has become more ambitious. Here are some of the major things we’recurrently work...
The Binary Analysis Platform Blog
At Jane Street, we enjoy using OCaml for lots of different things, from FPGA designs to web development. When it comes to Machine Learning, Python is one of the most commonly used languages. Machine learning frameworks such as TensorFlow or PyTorch wrap some highly efficient C++ and GPU implementations of tensor operations in easy to use Python apis. These frameworks also provide automatic differentiation functionalities which are commonly used to train deep learning models. In this talk we see how we can leverage TensorFlow or PyTorch directly from OCaml so that we can use our favorite programming language to build deep learning models and train them on GPUs. We will consider the Reinforcement Learning setting where an agent is trained to play Atari video games such as Space Invaders or Breakout. Our agents will be based on the Deep Q-Learning approach introduced in 2014. Laurent Mazare Laurent first joined Jane Street as a developer in the London office back in 2013 working on trading systems. After a short stint at DeepMind in 2017/2018, he is now back at Jane Street as a researcher working on the equities desk in London. Laurent holds a PhD in theoretical computer science from Institut National Polytechnique de Grenoble.
