![]() (This comes from the novelized version of her life Sadako and the Thousand Paper Cranes.) However, an exhibit that appeared in the Hiroshima Peace Memorial Museum stated that by the end of August 1955, Sasaki had achieved her goal and continued to fold 300 more cranes. Her best friend, Chizuko, also brought paper from school for Sasaki to use.Ī popular version of the story is that Sasaki fell short of her goal of folding 1,000 cranes, having folded only 644 before her death and that her friends completed the 1,000 and buried them all with her. Although she had plenty of free time during her days in the hospital, Sasaki lacked paper, so she used medicine wrappings and whatever else she could scrounge including going to other patients' rooms to ask for the paper from their get-well presents. Sasaki's friend, Chizuko Hamamoto, told her the legend of the cranes and she set herself a goal of folding 1,000 of them, which was believed to grant the folder a wish. Shortly after, cranes were brought to her room from a local high school club. In August 1955, she was moved into a room with a girl named Kiyo, a junior high school student who was two years older than her. By the time she was admitted, her white blood cell count was six times higher than the average child's levels. She was admitted as a patient to the Hiroshima Red Cross Hospital for treatment and given blood transfusions on February 21, 1955. By the early 1950s, it was clear that the leukemia was caused by radiation exposure by the uranium in the bomb. Several years after the atomic explosion an increase in leukemia was observed, especially among children. She was hospitalized on February 21, 1955, and given no more than a year to live. Subsequently, she was diagnosed with acute malignant lymph gland leukemia (her mother and others in Hiroshima referred to it as "atomic bomb disease"). In January 1955, purpura had formed on her legs. In November 1954, Sadako developed swellings on her neck and behind her ears. Sadako grew up like her peers and became an important member of her class relay team. Her grandmother ran back inside and died near the house, apparently trying to escape fires by hiding in a cistern. While they were fleeing, Sadako and her mother were caught in black rain. She was blown out of the window and her mother ran out to find her, suspecting she may be dead, but instead finding her two-year-old daughter alive with no apparent injuries. Sadako Sasaki was at home, about 1.6 kilometres (1 mi) away from ground zero, when the United States dropped an atomic bomb on Hiroshima. She died at the age of 12 on Octoat the Hiroshima Red Cross Hospital. She is remembered through the story of the more than one thousand origami cranes she folded before her death. She survived for another ten years, becoming one of the most widely known hibakusha-a Japanese term meaning "bomb-affected person". She was two years of age when the bombs were dropped and was severely irradiated. Sadako Sasaki ( 佐々木 禎子, Sasaki Sadako, Janu– October 25, 1955) was a Japanese girl who became a victim of the atomic bombings of Hiroshima and Nagasaki by the United States.
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Some examples would be if there is too much noise in your training data (outliers, erroneous attribute values, etc.) - which cannot be compensated for with data preprocessing - or use in unsupervised learning (as we will discuss later). However, there may be cases where these traditional/main loss functions may not be sufficient. In cases where the number of classes is greater than two, we utilize categorical cross-entropy - this follows a very similar process to binary cross-entropy. def log_loss (y_true, y_pred): y_pred = tf.clip_by_value(y_pred, le-7, 1 - le-7) error = y_true * tf.log(y_pred + 1e-7) (1-y_true) * tf.log(1-y_pred + 1e-7) return -error Categorical Cross-Entropy Loss Thus, to accurately determine loss between the actual and predicted values, it needs to compare the actual value (0 or 1) with the probability that the input aligns with that category ( p(i) = probability that the category is 1 1 - p(i) = probability that the category is 0) In binary classification, there are only two possible actual values of y - 0 or 1. One of the most popular loss functions, MSE finds the average of the squared differences between the target and the predicted outputsĬlassification neural networks work by outputting a vector of probabilities - the probability that the given input fits into each of the pre-set categories then selecting the category with the highest probability as the final output. 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Real projectiles launched on earth experience air resistance. We know that if the air resistance is neglected in the projectile motion, the horizontal component of the force acting on the projectile at any given time will be zero, which means that $$ as the velocity in horizontal direction. ![]() At low speeds, for objects with a high ratio of weight to surface area (like, not a feather or a sheet of paper), we can ignore air resistance. At high speeds, even an aerodynamic bullet experiences a lot of air resistance. With zero air drag force, the analytic solution is well known. This means that if the derivative of horizontal velocity is zero, then the horizontal velocity does not change with time which means it is constant.Thus, in the projectile motion, if air resistance is ignored, the horizontal motion is at constant velocity. The trajectory of the projectile is a parabola. 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