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Hace 5 días · Design thinking is an approach to generating ideas that, at its core, puts humans first. Instead of focusing on the problem, this solution-oriented method considers the user. Basing the solution on an empathetic understanding of the user’s needs and how the proposed solution might impact them shifts how you think about addressing the situation.
Hace 5 días · El Design Thinking se compone de cinco fases que guían el proceso desde la comprensión del problema hasta la implementación de la solución. Empatizar: en esta primera fase se busca conocer a fondo las necesidades y comportamientos del usuario. Para ello se requieren entrevistas a clientes y empleados y análisis de datos para identificar ...
Hace 2 días · Hierarchical clustering is an unsupervised learning technique for grouping similar objects into clusters. It creates a hierarchy of clusters by merging or splitting them based on similarity measures. It uses a bottom-up approach or top-down approach to construct a hierarchical data clustering schema.
Hace 4 días · Alternative Hypothesis (H1): Represents what you want to prove—typically, that there is an effect or a difference. Example: Testing if a new tire compound has a mean durability greater than 50,000 kilometers. Null Hypothesis: Mean durability = 50,000 km (H0). Alternative Hypothesis: Mean durability > 50,000 km (H1).
Hace 4 días · Gaussian Mixture Models are a powerful clustering algorithm. Understand how Gaussian Mixture Models work and how to implement them in Python. We’ll also cover the k-means clustering algorithm and see how Gaussian Mixture Models improve on it.
Hace 4 días · En un nuevo episodio de MultiVersos por las multiplataformas de TVU, conversamos con Jorge Sanhueza, mentor de IncubaUdeC, sobre la metodología del Design Thinking, que trata sobre generar soluciones a partir de un reto. Se divide en cinco fases: Empatizar: Aprende de la audiencia para la cual estás trabajando.
Hace 4 días · The authors of propose a low-complexity learning-based user clustering method using an improved mean-shift clustering algorithm, which effectively utilizes the degrees of freedom in the system to form NOMA clusters and significantly enhances performance.
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