Accelerated Python: Fix bad syllabi link.

Author: brycelelbach

tutorials/accelerated-python/notebooks/syllabi/cuda_python__cuda_core_cccl__2_hours.md

@@ -0,0 +1,27 @@
+## CUDA Python Tutorial - cuda.core & CCCL - 2 Hours
+
+### Notebooks
+
+| # | Topic | Solution | Technologies |
+|---|-------|----------|--------------|
+| 00 | [What are GPUs?](../fundamentals/00__what_are_gpus.ipynb) | - | - |
+| 07 | [cuda.core: Devices, Streams and Memory](../fundamentals/07__cuda_core__devices_streams_and_memory.ipynb) | [Click](../fundamentals/solutions/07__cuda_core__devices_streams_and_memory__SOLUTION.ipynb) | cuda.core |
+| 23 | [CCCL: Customizing Algorithms](../libraries/23__cuda_cccl__customizing_algorithms.ipynb) | [Click](../libraries/solutions/23__cuda_cccl__customizing_algorithms__SOLUTION.ipynb) | cuda-cccl |
+
+### Materials
+
+<table><tr>
+<td><a href="https://github.com/NVIDIA/accelerated-computing-hub/blob/generated/main/tutorials/accelerated-python/notebooks/syllabi/cuda_python__cuda_core_cccl__2_hours__docker_compose.yml">Docker Compose for Brev Launchable</a></td>
+</tr></table>
+
+### Introduction
+
+In this tutorial we will cover:
+- What is a GPU and why is it different to a CPU?
+- An overview of the CUDA development model.
+- The CUDA Python ecosystem.
+- Working with NumPy / CuPy style arrays on the GPU.
+- Writing CUDA kernels using cuda.core.
+- Using cuda.compute
+
+Attendees will be expected to have a general knowledge of Python and programming concepts, as well as a basic understanding of GPU computing.