In-place mutating updates of arrays, like x[i] += y, aren't supported, but there are functional alternatives. Under a jit, those functional alternatives will reuse buffers in-place automatically. Random numbers are different, but for good reasons. If you're looking for convolution operators,...
In-place mutating updates of arrays, likex[i] += y, aren't supported, butthere are functional alternatives. Under ajit, those functional alternatives will reuse buffers in-place automatically. Random numbers are different, but forgood reasons. ...
and morphology and electrotonic properties of these MNs are expected to have high impact on their physiological properties, we expected morphological and electrical differences between limb moving MNs in a location dependent manner along the spinal cord....
I communicate between Python and Objective C, and the Objective C msgpack library is totally broken because the string type is missing; in fact, the Objective C object/dictionary standard construct must have strings as keys, and thus the msgpack Objective C library tries to convert everything in...
In-place mutating updates of arrays, likex[i] += y, aren't supported, butthere are functional alternatives. Under ajit, those functional alternatives will reuse buffers in-place automatically. Random numbers are different, but forgood reasons. ...
In-place mutating updates of arrays, like x[i] += y, aren't supported, but there are functional alternatives. Under a jit, those functional alternatives will reuse buffers in-place automatically. Random numbers are different, but for good reasons. If you're looking for convolution operators,...
In-place mutating updates of arrays, like x[i] += y, aren't supported, but there are functional alternatives. Under a jit, those functional alternatives will reuse buffers in-place automatically. Random numbers are different, but for good reasons. If you're looking for convolution operators,...
NumPy also does a lot of work to cast any array-like function arguments to arrays, as in np.sum([x, y]), while jax.numpy typically requires explicit casting of array arguments, like np.sum(np.array([x, y])). For automatic differentiation with grad, JAX has the same restrictions as...
In-place mutating updates of arrays, like x[i] += y, aren't supported, but there are functional alternatives. Under a jit, those functional alternatives will reuse buffers in-place automatically. Random numbers are different, but for good reasons. If you're looking for convolution operators,...
NumPy also does a lot of work to cast any array-like function arguments to arrays, as in np.sum([x, y]), while jax.numpy typically requires explicit casting of array arguments, like np.sum(np.array([x, y])). For automatic differentiation with grad, JAX has the same restrictions as...