principle of segmentation and pre-training
Pre-training
Theoretical Fundaments:
Segmentation and pre-training are fundamental techniques used to improve the performance of machine learning models.
This section highlights the theoretical foundations and practical benefits of training a model on a large dataset before using it for a specific task.
Segmentation relies on the concept of inductive bias, which suggests that learning algorithms prioritize certain types of information over others. By segmenting data, the model focuses on specific aspects relevant to the task.
• Theoretical Fundaments
- Pre-training builds upon the principle of transfer learning. By learning general representations from a large unlabeled dataset, the model can leverage this knowledge when tackling a specific task (even if the datasets are different).
Segmentation
Practical Applications
- breaks Down complexs data into smaller more manageable pieces
• Practical Applications
Pre-training
Pre-training improves efficiency by allowing the model to focus on learning task-specific details rather than basic features it has already learned.
Segmentation improves the accuracy of downstream tasks (tasks the model is ultimately trained for) by making data more manageable and allowing the model to extract more relevant features. It can be used for tasks like image segmentation (identifying objects within an image) or text segmentation (identifying sentences or paragraphs).
- Learns general representation from large unlabeled datasets
Pre-training
Segmentation
- improves accuracy of dowwnstream task
- Improves efficiency by leveraging pre-learned features
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principle of segmentation and pre-training
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Transcript
principle of segmentation and pre-training
Pre-training
Theoretical Fundaments:
Segmentation and pre-training are fundamental techniques used to improve the performance of machine learning models.
This section highlights the theoretical foundations and practical benefits of training a model on a large dataset before using it for a specific task.
Segmentation relies on the concept of inductive bias, which suggests that learning algorithms prioritize certain types of information over others. By segmenting data, the model focuses on specific aspects relevant to the task.
• Theoretical Fundaments
Segmentation
Practical Applications
• Practical Applications
Pre-training
Pre-training improves efficiency by allowing the model to focus on learning task-specific details rather than basic features it has already learned.
Segmentation improves the accuracy of downstream tasks (tasks the model is ultimately trained for) by making data more manageable and allowing the model to extract more relevant features. It can be used for tasks like image segmentation (identifying objects within an image) or text segmentation (identifying sentences or paragraphs).
Pre-training
Segmentation
Lista Triángulos
Lorem ipsum dolor sit amet, lorem salutandi eu mea, eam in soleat iriure assentior. Tamquam lobortis id qui. Ea sanctus democritum mei
Escribe un título
Escribe un título
Lorem ipsum dolor sit amet
Lorem ipsum dolor sit amet
Escribe un título
Lorem ipsum dolor sit amet
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Usa este espacio para añadir una interactividad genial. Incluye texto, imágenes, vídeos, tablas, PDFs… ¡incluso preguntas interactivas! Tip premium: Obten información de cómo interacciona tu audiencia: