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Women's
In India, women face several significant challenges in achieving gender equality, including: 1. Cultural Norms and Stereotypes: Deeply ingrained cultural norms and stereotypes often dictate traditional gender roles, limiting women's opportunities in education, employment, and leadership. 2. WorkplacRead more
In India, women face several significant challenges in achieving gender equality, including:
1. Cultural Norms and Stereotypes: Deeply ingrained cultural norms and stereotypes often dictate traditional gender roles, limiting women’s opportunities in education, employment, and leadership.
2. Workplace Discrimination: Women frequently encounter bias and discrimination in the workplace, including unequal pay, limited career advancement opportunities, and gendered expectations regarding work-life balance.
3. Education Access: Although progress has been made, disparities in access to quality education for girls, particularly in rural or marginalized communities, continue to hinder their future prospects.
4. Violence and Harassment: Gender-based violence and harassment, both in public and private spheres, remain pervasive issues that undermine women’s safety, health, and autonomy.
Addressing these challenges requires concerted efforts from governments, civil society, and individuals to promote gender-sensitive policies, raise awareness, and create an environment where women can thrive equally.
See lessExplain the significance of trauma in the novel, "A Thousand Splendid Suns" by Khaled Hosseini.
In "A Thousand Splendid Suns" by Khaled Hosseini, trauma is a central theme that shapes the lives of the characters and drives the narrative. The novel portrays the personal and collective traumas experienced by Mariam and Laila, two women from different backgrounds whose lives converge in war-tornRead more
In “A Thousand Splendid Suns” by Khaled Hosseini, trauma is a central theme that shapes the lives of the characters and drives the narrative. The novel portrays the personal and collective traumas experienced by Mariam and Laila, two women from different backgrounds whose lives converge in war-torn Afghanistan. Mariam endures a childhood marked by illegitimacy and neglect, followed by an abusive marriage to Rasheed. Laila, on the other hand, suffers the loss of her family and the brutality of life under the Taliban regime.
The trauma they endure highlights the oppressive social and political conditions women face, and their resilience underscores the human capacity to find strength in adversity. The bond that forms between Mariam and Laila becomes a source of healing and empowerment, illustrating how shared suffering can foster solidarity and hope. Hosseini uses their experiences to emphasize the enduring impact of trauma and the possibility of redemption and resilience.
See less"You're tasked with building a recommendation system for a streaming platform. How would you approach this problem? What metrics would you use to evaluate the performance of your model?"
To build a recommendation system for a streaming platform, I would start by selecting the appropriate algorithm, such as collaborative filtering, content-based filtering, or a hybrid approach. Collaborative filtering uses user behavior and preferences to suggest content, while content-based filterinRead more
To build a recommendation system for a streaming platform, I would start by selecting the appropriate algorithm, such as collaborative filtering, content-based filtering, or a hybrid approach. Collaborative filtering uses user behavior and preferences to suggest content, while content-based filtering focuses on the characteristics of the items themselves.
I would begin by gathering and preprocessing data, such as user ratings, watch history, and metadata of the content. The next step would be to train the model using techniques like matrix factorization for collaborative filtering or TF-IDF for content-based filtering. I would also incorporate user-item interaction features and contextual data like time of day or device used.
To evaluate the performance, I would use metrics such as Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) for rating prediction accuracy, and precision, recall, and F1-score for recommendation relevance. Additionally, I would track user engagement metrics, like click-through rate (CTR) and conversion rate, to assess the real-world effectiveness of the recommendations.
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