From aa7dd2fcd8487c0f6fccc584444814ba3bab2242 Mon Sep 17 00:00:00 2001
From: Valerio Maggio <leriomaggio@users.noreply.github.com>
Date: Wed, 4 Dec 2024 12:24:29 +0000
Subject: [PATCH] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 6a695f1..acbe7bf 100644
--- a/README.md
+++ b/README.md
@@ -36,7 +36,7 @@ Please find below all the contributed resources, organised by category
   - [SyftBox Computational Model](https://syftbox-documentation.openmined.org/computation-model) - How computation works on SyftBox, in a nutshell
   - [Federated CPU Tracker Member (part1)](https://syftbox-documentation.openmined.org/cpu-tracker-1) - An example of SyftBox API that monitors local CPU usage and shares a private/sanitized version of the data within the SyftBox federated network.
   - [Federated CPU Tracker Leader (part 2)](https://syftbox-documentation.openmined.org/cpu-tracker-2) - A SyftBox API that aggregates CPU data from all members contributing to the computation, and creates a live visualization dashboard.
-  - [Getting Started with Federated Learning on SyftBox]([https://syftbox-documentation.openmined.org/tutorials/federated-learning/](https://syftbox-documentation.openmined.org/tutorials/federated-learning/getting-started/)) - A complete federated learning workflow for MNIST digit classification using SyftBox.
+  - [Getting Started with Federated Learning on SyftBox](https://syftbox-documentation.openmined.org/tutorials/federated-learning/getting-started/) - A complete federated learning workflow for MNIST digit classification using SyftBox.
   - [Ring Computation Walkthrough: Calculating An Average Across Nodes](https://github.com/flow254/FLFun/blob/main/create-average-ring-computation.md) - A brief walkthrough on creating a Ring Computation on SyftBox that computes the average value from nodes.
 
 - [From Centralised to Decentralised Training: An Intro to Federated Learning](https://github.com/deep-learning-indaba/indaba-pracs-2024/tree/main/practicals/Federated_Learning) - A Jupyter Notebook tutorial aimed to provide a practical overview with code examples to all the the foundational concepts tackled in federated learning. This tutorial was written by Andrej Jovanović, Sree Harsha Nelaturu and Luca Powell and presented at the 2024 iteration of the Deep Learning Indaba.