allcoding1_official
前往频道在 Telegram
📈 Telegram 频道 allcoding1_official 的分析概览
频道 allcoding1_official (@allcoding1_official) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 85 822 名订阅者,在 技术与应用 类别中位列第 1 503,并在 印度 地区排名第 3 558 位。
📊 受众指标与增长动态
自 невідомо 创建以来,项目保持高速增长,吸引了 85 822 名订阅者。
根据 17 六月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 -1 568,过去 24 小时变化为 -42,整体触达仍然可观。
- 认证状态: 未认证
- 互动率 (ER): 平均受众互动率为 3.64%。内容发布后 24 小时内通常能获得 0.82% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 3 127 次浏览,首日通常累积 701 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 2。
- 主题关注点: 内容集中在 dsa, stack, namaste, javascript, dev 等核心主题上。
📝 描述与内容策略
尚未提供频道描述。
凭借高频更新(最新数据采集于 18 六月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 技术与应用 类别中的关键影响点。
85 822
订阅者
-4224 小时
-3887 天
-1 56830 天
帖子存档
85 822
. Siemens Healthineers
https://jobs.siemens-healthineers.com/careers/job/563156126453725?microsite=siemens-healthineers&hl=en&utm_source=linkedin&domain=siemens.com&sourceType=PREMIUM_POST_SITE&source=LinkedIn
2. Wells Fargo
https://www.wellsfargojobs.com/en/jobs/r-389220/intern-analyst/?jClickId=715b6125-a409-4ab1-8d52-ba139399ad02&publisher=Linkedin%20Flat%20bid&utm_source=joveo
3. BNP Paribas
https://group.bnpparibas/en/careers/job-offer/data-science-intern?SRC=LINKEDIN
85 822
Cognizant is hiring IT analyst Trainee
Apply link 👇
https://app.joinsuperset.com/join/#/signup/student/jobprofiles/d0dc8ef3-a27b-4cd7-9274-5b7d4638f8d1
85 822
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85 822
Kill the enemy
C++
Amazon 1.15 PM
#include <vector>
#include <algorithm>
int solve(std::vector<int> &A, int B) {
long long m1 = 0, m2 = 0;
for (int val : A) {
if (val > m1) {
m2 = m1;
m1 = val;
} else if (val > m2) {
m2 = val;
}
}
long long b = B;
long long s = m1 + m2;
if (s == 0) {
return b > 0 ? -1 : 0;
}
long long k = b / s;
int ans = k * 2;
long long rem = b % s;
if (rem == 0) {
return ans;
} else if (rem <= m1) {
return ans + 1;
} else {
return ans + 2;
}
}
85 822
M1: Encode sequential order
T1: Regularization
M16: Likelihood × Prior
ML2: SGD can escape local minima due to its noisy updates
ML3: Recursive Feature Elimination (RFE)
ML4: Gini Index
M4: It overfits the training data
M7: 0
M2: Strong negative linear relationship
S13: No real solution
S17: Local minimum
S23: Converges by Limit Comparison with 1/n²
S30: Collect recent user data and evaluate model drift
S31: Data leakage inflated model performance
S39: 6/216
Amazon ML School MCQ Answers 1:15 PM
85 822
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85 822
S2: 4xy
S7: 6
S12: 0
Q4: 1/2
S22: (2, 3)
S28: Target/Mean Encoding
S29: TimeSeriesSplit
S38: 15
S43: 2/5
S59: Mode > Median > Mean
ML - 2: The data has a Gaussian distribution
ML - 7: Updating prior beliefs with observed data using Bayes' theorem
ML - 12: The probability distribution over actions given states
ML - 17: Internal covariate shift
ML - 23: Boosting reduces bias, bagging reduces variance
ML - 24: Binary Cross-Entropy
S48: 30/84
S60: 150
S53: 2/3
S68: Prior × Likelihood
85 822
S2: 4xy
S7: 6
S12: 0
Q4: 1/2
S22: (2, 3)
S28: Target/Mean Encoding
S29: TimeSeriesSplit
S38: 15
S43: 2/5
S59: Mode > Median > Mean
ML - 2: The data has a Gaussian distribution
ML - 7: Updating prior beliefs with observed data using Bayes' theorem
ML - 12: The probability distribution over actions given states
ML - 17: Internal covariate shift
ML - 23: Boosting reduces bias, bagging reduces variance
ML - 24: Binary Cross-Entropy
S48: 30/84
S60: 150
S53: 2/3
S68: Prior × Likelihood
Amazon ML School 100% Correct MCQ Answers 10:30 AM
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