<in this project> appeared twice

gsoc.md

@@ -95,7 +95,7 @@ The AD engine will be used for the typical DNN architectures.
 
 ### Description
 
-The family of reverse-mode AD engines in Julia consists mostly of Zygote, Enzyme and the upcoming Diffractor. These packages operate on the intermediate representation (IR) output of the compiler. They are very complex, and it takes many months or years to develop the specialized knowledge required to build these tools. As a result, fixing bugs or adding features is a time consuming task for non-expert developers. In this project, we will develop a "lower-tech" tape-based AD engine, in the spirit of Tracker.jl, which will be easier to maintain while offering fewer features than the existing, complex engines.
+The family of reverse-mode AD engines in Julia consists mostly of Zygote, Enzyme and the upcoming Diffractor. These packages operate on the intermediate representation (IR) output of the compiler. They are very complex, and it takes many months or years to develop the specialized knowledge required to build these tools. As a result, fixing bugs or adding features is a time consuming task for non-expert developers. 
 
 In this project we aim to solve this problem by using a simple and yet very effective approach: tapes. Tape based automated differentiation is in use in PyTorch, Tensorflow, and Jax. Despite their simplicity, taped-based ADs are the main tool in such successful deep learning frameworks. While PyTorch, Tensorflow and Jax are monoliths, the FluxML ecosystem consists of several packages and a new AD engine can be added quite easily. We will make use of the excellent ChainRules and NNlib packages and make the AD integrate with Flux.jl and Lux.jl.