https://cs.stanford.edu/~aozdemir/blog/unsafe-rust-syntax/
Alex Ozdemir Home Teaching Software Blog Research Personal Talks Despite the fundamental role unsafe plays in Rust, we have relatively little understanding of how it is being used in real codebases. As the community decides what the exact semantics of unsafe should be, it becomes increasingly important to have this understanding in order to avoid accidentally diverging from the expectations of library writers. This post takes a first step in that direction by laying the basis for syntactic analyses of unsafe in Rust code hosted on crates.io. Rust is a new systems programming language that seems to promise the world: all the control of C/C++ as well as all of the safety and convenience of your favorite high-level language. At its heart is a statically verified system of memory management - an incarnation of the RAII pattern baked into the type system it...
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ConvNetJS This demo follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning , a paper from NIPS 2013 Deep Learning Workshop from DeepMind. The paper is a nice demo of a fairly standard (model-free) Reinforcement Learning algorithm (Q Learning) learning to play Atari games. In this demo, instead of Atari games, we'll start out with something more simple: a 2D agent that has 9 eyes pointing in different angles ahead and every eye senses 3 values along its direction (up to a certain maximum visibility distance): distance to a wall, distance to a green thing, or distance to a red thing. The agent navigates by using one of 5 actions that turn it different angles. The red things are apples and the agent gets reward for eating them. The green things are poison and the agent gets negative reward ...
ConvNetJS This demo follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning , a paper from NIPS 2013 Deep Learning Workshop from DeepMind. The paper is a nice demo of a fairly standard (model-free) Reinforcement Learning algorithm (Q Learning) learning to play Atari games. In this demo, instead of Atari games, we'll start out with something more simple: a 2D agent that has 9 eyes pointing in different angles ahead and every eye senses 3 values along its direction (up to a certain maximum visibility distance): distance to a wall, distance to a green thing, or distance to a red thing. The agent navigates by using one of 5 actions that turn it different angles. The red things are apples and the agent gets reward for eating them. The green things are poison and the agent gets negative reward ...