The OCaml Planet

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.

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.

Nous sommes fiers d’annoncer la release (mineure) d’ opam 2.0.5. Cette nouvelle version contient des mises à jours de build et correctifs. Plus d’information...