想通过比赛晋级专家,要至少获得两个铜牌,也就是拿前10%的成绩,拿Datasets,Notebooks, Discussions的专家,因为更简单一些,要求就会高一些,它们的牌子主要是靠其他用户的点赞来获取的,点赞越多,越容易拿牌子,这也是kaggle的分享氛围非常好的原因之一,天池就没有这一套系统。
最简单直接方法就是直接点进去这个数据值的界面进行查看，但是在这个过程中我们需要额外关注以下几点的信息：首先是数据值的简介，这个可以在data card-about datasets这个栏当中找到。第二个数据值的大小。因为大部分的数据集它会告诉你它有多少的特征，也就是多少列也会显示包含了多少的样本，也就是多少行。那么有...
Caffe，Keras等等，只要对其中1个有比较深入的了解，打Kaggle比赛基本没有任何问题。
Leverage Kaggle's models, notebooks & datasets. Researchers Advance ML with our pre-trained model hub & competitions. expand_moreSee more Tackle your next project with Kaggle On Kaggle you'll find all the resources and knowledge needed for your next real-world ML project. ...
We find that most of the Kaggle datasets are characterized by higher intermittence and entropy than the M-competitions and that global ensemble models tend to outperform local single models. Furthermore, we find the strong performance of gradient boosted decision trees, increasing success of neural...
image = image /255.image = tf.cast(image, tf.float32)returnimage, gender# Obtain training, testing and validation datasetstrain_ds = tfds.Dataset.from_tensor_slices((train_images, train_ages, train_genders)).shuffle(2000) train_age_ds = train_ds.map(preprocess_age_data, num_parallel_call...
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1-(2). Fashion MNIST Classification and Visualization |Github Step 1. Library Import & Load Dataset 1-1. Library 1-2. Datasets 1-3. Shape of Data 1-4. Show Image Step 2. Data Preprocessing 2-1. class name (y) 2-2. Normalization (X) 2-3. Visualization (X) Step 3. Modeling Ste...
Data visualization can suggest transformations, often a "reshaping" of a feature through powers or logarithms. 对于偏斜数据我们可以通过对数化以达到归一化: # 这两行代码是忽略warning信息的,可写可不写 # import warnings # warnings.filterwarnings('ignore') sns.kdeplot(accidents.WindSpeed, shade=True...
All datasetsComputer ScienceEducationClassificationComputer VisionNLPData VisualizationPre-Trained Model insights Trending Datasets See All Mohammad1ziar· Updated4 days ago Usability10.0· 4 MB 1 File (CSV) arrow_drop_up13 bar_chart Benchmark datasets ...