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chapters/en/unit1/chapter1/definition.mdx

@@ -16,7 +16,7 @@ The evolution of computer vision has been marked by a series of incremental adva
 
 Initially, to extract and learn information in an image, you extract features through image-preprocessing techniques (Pre-processing for Computer Vision Tasks). Once you have a group of features describing your image, you use a classical machine learning algorithm on your dataset of features. It is a strategy that already simplifies things from the hard-coded rules, but it still relies on domain knowledge and exhaustive feature engineering. A more state-of-the-art approach arises when deep learning methods and large datasets meet. Deep learning (DL) allows machines to automatically learn complex features from the raw data. This paradigm shift allowed us to build more adaptive and sophisticated models, causing a renaissance in the field.
 
-The seeds of computer vision were sown long before the rise of deep learning models during 1960's, pioneers like David Marr and Hans Moravec wrestled with the fundamental question: Can we get machines to see? Early breakthroughs like edge detection algorithms, object recognition were achived with a mix of cleverness and brute-force which laid the ground work for this developing computer vision systems. Over time, as research and development advanced and hardware capabilities improved, the computer vision community expanded exponentially. This vibrant community is composed of researchers,engineers, data scientists, and passionate hobbyists across the globe coming from a vast arrayof disciplines. With open-source and community driven projects we are witnessing democratized access to cutting-edge tools and technologies helping to create a renaissance in this field.
+The seeds of computer vision were sown long before the rise of deep learning models during 1960's, pioneers like David Marr and Hans Moravec wrestled with the fundamental question: Can we get machines to see? Early breakthroughs like edge detection algorithms, object recognition were achieved with a mix of cleverness and brute-force which laid the ground work for this developing computer vision systems. Over time, as research and development advanced and hardware capabilities improved, the computer vision community expanded exponentially. This vibrant community is composed of researchers,engineers, data scientists, and passionate hobbyists across the globe coming from a vast array of disciplines. With open-source and community driven projects we are witnessing democratized access to cutting-edge tools and technologies helping to create a renaissance in this field.
 
 ## Interdisciplinary with other fields and Image Understanding