diff --git a/lectures/_config.yml b/lectures/_config.yml index 9fde9a122..c60ac8dc0 100644 --- a/lectures/_config.yml +++ b/lectures/_config.yml @@ -98,6 +98,9 @@ sphinx: advanced: - "https://python-advanced.quantecon.org" - null + jax: + - "https://jax.quantecon.org/" + - null mathjax3_config: tex: macros: diff --git a/lectures/newton_method.md b/lectures/newton_method.md index 4f2788cf1..f6b8a6733 100644 --- a/lectures/newton_method.md +++ b/lectures/newton_method.md @@ -29,8 +29,7 @@ kernelspec: ``` ```{seealso} -**GPU:** A version of this lecture which makes use of [jax](https://jax.readthedocs.io) to run the code -on a `GPU` is [available here](https://jax.quantecon.org/newtons_method.html) +A version of this lecture using [JAX](https://github.com/jax-ml/jax) is {doc}`available here ` ``` ## Overview @@ -790,7 +789,7 @@ With the larger overhead, the speed is not better than the optimized `scipy` fun Our next step is to investigate a large market with 3,000 goods. A JAX version of this section using GPU accelerated linear algebra and -automatic differentiation is available [here](https://jax.quantecon.org/newtons_method.html#application) +automatic differentiation is {doc}`available here ` The excess demand function is essentially the same, but now the matrix $A$ is $3000 \times 3000$ and the parameter vectors $b$ and $c$ are $3000 \times 1$. diff --git a/lectures/wealth_dynamics.md b/lectures/wealth_dynamics.md index bdd5d7d8f..f8d544346 100644 --- a/lectures/wealth_dynamics.md +++ b/lectures/wealth_dynamics.md @@ -24,8 +24,7 @@ kernelspec: ``` ```{seealso} -A [version of this lecture](https://jax.quantecon.org/wealth_dynamics.html) using a `GPU` -is [available here](https://jax.quantecon.org/wealth_dynamics.html) +A version of this lecture using [JAX](https://github.com/jax-ml/jax) is {doc}`available here ` ``` In addition to what's in Anaconda, this lecture will need the following libraries: