Seems you have not registered as a member of localhost.saystem.shop!
You may have to register before you can download all our books and magazines, click the sign up button below to create a free account.
The Athlete's Covenant
Using everyday sporting experiences as a foundation, Suzanne Stefanowski Hudd lays out a set of informal rules that athletic team members learn to uphold. Prescribed within the “athlete’s covenant,” these guidelines support the transformation of the player’s individual commitment to hard work into a set of collective, role-related obligations that are applicable across time and sport. Hudd’s analysis highlights sportsmanship as it is practiced daily, flowing naturally from the mimicry and synchrony that players routinely use to perfect their talents. Working to turn star players into team players, the covenant encourages athletes to set their sights on goals that surpass what their individual talents alone can provide. Hudd theorizes our waning commitment to these important collectivistic properties of sport has contributed to the belief that sportsmanship is a thing of the past.
The Athlete's Covenant
In this in-depth study of sportsmanship and athletic teams, Suzanne S. Hudd analyzes the athlete's covenant, a set of informal guidelines that remind athletes they play for others as much as themselves. Hudd studies the waning commitment to the collectivistic properties of sport and the growing belief that sportsmanship is a thing of the past.
Trends in Deep Learning Methodologies
Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, comp...
