Update dead Youtube links.

learn.md

@@ -5,36 +5,46 @@ title = "Learn"
 # Learn
 
 Currently, the Julia CUDA stack is the most mature, easiest to install, and
-full-featured.
-The [CUDA.jl documentation](https://cuda.juliagpu.org/stable/) is
-a central place for information on all relevant packages.
-Start with the instructions on [how to install](https://cuda.juliagpu.org/stable/installation/overview/)
-the stack, and follow with this [introductory tutorial](https://cuda.juliagpu.org/stable/tutorials/introduction/).
+full-featured. The [CUDA.jl documentation](https://cuda.juliagpu.org/stable/) is
+a central place for information on all relevant packages. Start with the
+instructions on [how to
+install](https://cuda.juliagpu.org/stable/installation/overview/) the stack, and
+follow with this [introductory
+tutorial](https://cuda.juliagpu.org/stable/tutorials/introduction/). There are
+also a series of [notebooks on more advanced uses of
+CUDA.jl](https://github.com/JuliaGPU/Learning/tree/main/Courses/AdvancedCUDA),
+including application and kernel optimization, as well as advanced memory
+management and concurrent programming concepts (which apply to other back-ends
+as well).
 
-If you prefer videos, the presentations below highlight different aspects
-of the toolchain.
+If you prefer video material, there are plenty of talks and workshops on GPU
+programming in Julia to be found on Youtube. For example:
 
 
-## Concurrent GPU computing in CUDA.jl 3.0
+## GPU programming in Julia
 
-Introduction to concurrent GPU computing:
+3-hour workshop covering various of the toolchain:
 
-* Overlapping GPU computations
-* Using multiple devices
-* Using threads
+* Array programming
+* Kernel programminng
+* Parallel proggramming concepts
+* CUDA.jl application and kernel profiling
+* Image processing using AMDGPU.jl
+* Vendor-neutral GPU programming with KernelAbstractions.jl
 
-{{youtube fw0R5G8pB0U}}
+{{youtube Hz9IMJuW5hU}}
 
 \\
 
 
-## Effective CUDA GPU computing in Julia
+## Concurrent GPU computing in CUDA.jl 3.0
 
-* Design and benefits of the Julia GPU stack
-* Composability with existing (non-GPU) software
-* Performance killers and tools for optimization
-* Demonstration
+Introduction to concurrent GPU computing:
+
+* Overlapping GPU computations
+* Using multiple devices
+* Using threads
 
-{{youtube 7Yq1UyncDNc}}
+{{youtube fw0R5G8pB0U}}
 
 \\