Merge pull request #407 from tagny/patch-1

remove repetition of  word 'topics'

Author: johko

chapters/en/unit0/welcome/welcome.mdx

@@ -82,7 +82,7 @@ The course is organized into multiple units, covering the fundamentals and delvi
 - **Unit 6 - Basic Computer Vision Tasks** : cover fundamental tasks like image classification, object detection, and segmentation and the models used in them (YOLO, SAM). Gain insights into metrics and practical applications for these tasks.
 - **Unit 7 - Video and Video Processing** : examine the characteristics of videos, the role of video processing, and the challenges compared to image processing. Explore temporal continuity, motion estimation, and practical applications in video processing.
 - **Unit 8 - 3D Vision, Scene Rendering, and Reconstruction** : delve into the complexities of three-dimensional vision, exploring concepts like Nerf and GQN for scene rendering and reconstruction. Understand the challenges and applications of 3D vision in computer vision, and how it provides an even more comprehensive view of spatial information.
-- **Unit 9 - Model Optimization** : explore the critical aspects of model optimization. Cover techniques such as model compression, deployment considerations, and the usage of tools and frameworks. Include topics topics like distillation, pruning, and TinyML for efficient model deployment.
+- **Unit 9 - Model Optimization** : explore the critical aspects of model optimization. Cover techniques such as model compression, deployment considerations, and the usage of tools and frameworks. Include topics like distillation, pruning, and TinyML for efficient model deployment.
 - **Unit 10 - Synthetic Data Creation** : discover the importance of synthetic data creation using deep generative models. Explore methods like point clouds and diffusion models and investigate major synthetic datasets and their applications in computer vision.
 - **Unit 11 - Zero Shot Computer Vision** : delve into the realm of zero-shot learning in computer vision, covering aspects of generalization, transfer learning, and its applications in tasks such as zero-shot recognition and image segmentation. Explore the relationship between zero-shot learning and transfer learning across various computer vision domains.
 - **Unit 12 - Ethics and Biases in Computer Vision** : understand the ethical considerations specific to computer vision. Explore why ethics matter, how biases can infiltrate AI models, and the types of biases prevalent in these domains. Learn how to do bias evaluation and mitigation strategies, emphasizing responsible development and deployment of AI technologies.