关于“Performing Advanced Feature Engineering in Keras”的评价
正在加载…
未找到任何结果。

在 Google Cloud 控制台中运用您的技能

关于“Performing Advanced Feature Engineering in Keras”的评价

9481 条评价

ROBERTO O. · 已于 over 1 year前审核

George A. · 已于 over 1 year前审核

Manish L. · 已于 over 1 year前审核

Rahul M. · 已于 over 1 year前审核

Great practice

Free A. · 已于 over 1 year前审核

Zwivhuya N. · 已于 over 1 year前审核

Kamil K. · 已于 over 1 year前审核

Jun M. · 已于 over 1 year前审核

Miguel V. · 已于 over 1 year前审核

Dinesh A. · 已于 over 1 year前审核

Stephen B. · 已于 over 1 year前审核

Tianyi J. · 已于 over 1 year前审核

Yihao C. · 已于 over 1 year前审核

nice

Jj J. · 已于 over 1 year前审核

Rab N. · 已于 over 1 year前审核

Maria L. · 已于 over 1 year前审核

Brian A. · 已于 over 1 year前审核

Suchita B. · 已于 over 1 year前审核

Nikhil B. · 已于 over 1 year前审核

Tukaram B. · 已于 over 1 year前审核

csv dataset differs from the designed model. Please update the model design including with string types for hour and day as expected : <class 'pandas.core.frame.DataFrame'> RangeIndex: 7499 entries, 0 to 7498 Data columns (total 8 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 fare_amount 7499 non-null float64 1 passenger_count 7499 non-null int64 2 pickup_longitude 7498 non-null float64 3 pickup_latitude 7498 non-null float64 4 dropoff_longitude 7498 non-null float64 5 dropoff_latitude 7498 non-null float64 6 hourofday 7499 non-null int64 7 dayofweek 7499 non-null int64 dtypes: float64(5), int64(3) memory usage: 468.8 KB

christian s. · 已于 over 1 year前审核

Vishnu P. · 已于 over 1 year前审核

Hao N. · 已于 over 1 year前审核

BI F. · 已于 over 1 year前审核

Omar R. V. · 已于 over 1 year前审核

我们无法确保发布的评价来自已购买或已使用产品的消费者。评价未经 Google 核实。