2\u3001\u5728\u7ebf\u6559\u7a0b<\/strong><\/p>\n<\/p>\nPython\u793e\u533a\u4e2d\u6709\u5927\u91cf\u7684\u5728\u7ebf\u6559\u7a0b\u548c\u5b66\u4e60\u8d44\u6e90\uff0c\u4ece\u5165\u95e8\u5230\u9ad8\u7ea7\uff0c\u6db5\u76d6\u4e86\u4eba\u5de5\u667a\u80fd\u7684\u5404\u4e2a\u65b9\u9762\u3002\u8fd9\u4e9b\u8d44\u6e90\u5e2e\u52a9\u5f00\u53d1\u8005\u5feb\u901f\u638c\u63e1Python\u5728\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u5e94\u7528\u3002\u4f8b\u5982\uff0cCoursera\u3001edX\u548cUdacity\u7b49\u5e73\u53f0\u63d0\u4f9b\u4e86\u591a\u95e8Python\u548c\u4eba\u5de5\u667a\u80fd\u76f8\u5173\u7684\u8bfe\u7a0b\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u6613\u4e8e\u5b66\u4e60\u548c\u4f7f\u7528<\/p>\n<\/p>\n
Python\u7684\u8bed\u6cd5\u7b80\u6d01\u660e\u4e86\uff0c\u6613\u4e8e\u5b66\u4e60\u548c\u4f7f\u7528\uff0c\u4f7f\u5f97\u5f00\u53d1\u8005\u80fd\u591f\u5feb\u901f\u4e0a\u624b\u3002Python\u7684\u52a8\u6001\u7c7b\u578b\u548c\u5185\u7f6e\u6570\u636e\u7ed3\u6784\uff08\u5982\u5217\u8868\u3001\u5b57\u5178\u7b49\uff09\u4f7f\u5f97\u7f16\u5199\u4ee3\u7801\u66f4\u52a0\u9ad8\u6548\u3002\u540c\u65f6\uff0cPython\u8fd8\u652f\u6301\u9762\u5411\u5bf9\u8c61\u7f16\u7a0b\u548c\u51fd\u6570\u5f0f\u7f16\u7a0b\uff0c\u4e3a\u5f00\u53d1\u8005\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u7f16\u7a0b\u8303\u5f0f\u3002<\/p>\n<\/p>\n
1\u3001\u7b80\u6d01\u7684\u8bed\u6cd5<\/strong><\/p>\n<\/p>\nPython\u7684\u8bed\u6cd5\u8bbe\u8ba1\u7b80\u6d01\u660e\u4e86\uff0c\u4f7f\u5f97\u4ee3\u7801\u66f4\u6613\u8bfb\u6613\u5199\u3002\u76f8\u6bd4\u4e8e\u5176\u4ed6\u7f16\u7a0b\u8bed\u8a00\uff0cPython\u7684\u5b66\u4e60\u66f2\u7ebf\u8f83\u4e3a\u5e73\u7f13\uff0c\u9002\u5408\u521d\u5b66\u8005\u5feb\u901f\u5165\u95e8\u3002<\/p>\n<\/p>\n
2\u3001\u4e30\u5bcc\u7684\u5185\u7f6e\u51fd\u6570<\/strong><\/p>\n<\/p>\nPython\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5185\u7f6e\u51fd\u6570\uff0c\u4f7f\u5f97\u5e38\u89c1\u7684\u7f16\u7a0b\u4efb\u52a1\uff08\u5982\u5b57\u7b26\u4e32\u5904\u7406\u3001\u6587\u4ef6\u64cd\u4f5c\u7b49\uff09\u53d8\u5f97\u975e\u5e38\u7b80\u5355\u3002\u8fd9\u4e9b\u5185\u7f6e\u51fd\u6570\u5e2e\u52a9\u5f00\u53d1\u8005\u63d0\u9ad8\u5f00\u53d1\u6548\u7387\uff0c\u4e13\u6ce8\u4e8e\u5b9e\u73b0\u6838\u5fc3\u7b97\u6cd5\u548c\u529f\u80fd\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5e7f\u6cdb\u7684\u5e94\u7528\u9886\u57df<\/p>\n<\/p>\n
Python\u5728\u4eba\u5de5\u667a\u80fd\u7684\u5404\u4e2a\u9886\u57df\u90fd\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u4ece\u8ba1\u7b97\u673a\u89c6\u89c9\u5230\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff0c\u518d\u5230\u5f3a\u5316\u5b66\u4e60\uff0cPython\u90fd\u80fd\u80dc\u4efb\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u4ecb\u7ecd\u51e0\u4e2a\u5177\u4f53\u7684\u5e94\u7528\u5b9e\u4f8b\uff0c\u5c55\u793aPython\u5728\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u5f3a\u5927\u529f\u80fd\u3002<\/p>\n<\/p>\n
1\u3001\u8ba1\u7b97\u673a\u89c6\u89c9<\/strong><\/p>\n<\/p>\n\u8ba1\u7b97\u673a\u89c6\u89c9\u662f\u4eba\u5de5\u667a\u80fd\u7684\u4e00\u4e2a\u91cd\u8981\u5206\u652f\uff0c\u4e3b\u8981\u7814\u7a76\u5982\u4f55\u4f7f\u673a\u5668\u5177\u6709\u201c\u89c6\u89c9\u201d\u80fd\u529b\u3002\u901a\u8fc7Python\u7684OpenCV\u3001Pillow\u7b49\u5e93\uff0c\u5f00\u53d1\u8005\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u3002\u4f8b\u5982\uff0c\u4f7f\u7528OpenCV\u8fdb\u884c\u56fe\u50cf\u8bc6\u522b\u3001\u76ee\u6807\u68c0\u6d4b\u548c\u56fe\u50cf\u5206\u5272\u7b49\u4efb\u52a1\u3002<\/p>\n<\/p>\n
2\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406<\/strong><\/p>\n<\/p>\n\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u662f\u4eba\u5de5\u667a\u80fd\u7684\u53e6\u4e00\u4e2a\u91cd\u8981\u5206\u652f\uff0c\u4e3b\u8981\u7814\u7a76\u5982\u4f55\u4f7f\u673a\u5668\u7406\u89e3\u548c\u751f\u6210\u81ea\u7136\u8bed\u8a00\u3002Python\u7684NLTK\u3001spaCy\u548cTransformers\u7b49\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u548c\u6a21\u578b\uff0c\u4f7f\u5f97NLP\u4efb\u52a1\uff08\u5982\u5206\u8bcd\u3001\u8bcd\u6027\u6807\u6ce8\u3001\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\u7b49\uff09\u53d8\u5f97\u975e\u5e38\u7b80\u5355\u3002<\/p>\n<\/p>\n
3\u3001\u5f3a\u5316\u5b66\u4e60<\/strong><\/p>\n<\/p>\n\u5f3a\u5316\u5b66\u4e60\u662f\u673a\u5668\u5b66\u4e60\u7684\u4e00\u4e2a\u91cd\u8981\u5206\u652f\uff0c\u4e3b\u8981\u7814\u7a76\u667a\u80fd\u4f53\u5982\u4f55\u901a\u8fc7\u4e0e\u73af\u5883\u7684\u4ea4\u4e92\u6765\u5b66\u4e60\u6700\u4f18\u7b56\u7565\u3002Python\u7684Gym\u3001Stable-Baselines\u7b49\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u73af\u5883\u548c\u7b97\u6cd5\uff0c\u4f7f\u5f97\u5f00\u53d1\u8005\u80fd\u591f\u5feb\u901f\u5b9e\u73b0\u548c\u6d4b\u8bd5\u5f3a\u5316\u5b66\u4e60\u7b97\u6cd5\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5177\u4f53\u5e94\u7528\u5b9e\u4f8b<\/p>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u5c55\u793aPython\u5728\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u5e94\u7528\uff0c\u4e0b\u9762\u6211\u4eec\u901a\u8fc7\u51e0\u4e2a\u5177\u4f53\u7684\u5b9e\u4f8b\u6765\u8be6\u7ec6\u4ecb\u7ecd\u3002<\/p>\n<\/p>\n
1\u3001\u56fe\u50cf\u5206\u7c7b<\/strong><\/p>\n<\/p>\n\u56fe\u50cf\u5206\u7c7b\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\u7684\u4e00\u4e2a\u57fa\u672c\u4efb\u52a1\uff0c\u4e3b\u8981\u7814\u7a76\u5982\u4f55\u5c06\u56fe\u50cf\u5206\u4e3a\u4e0d\u540c\u7684\u7c7b\u522b\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Keras\u548cTensorFlow\u6765\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u6a21\u578b\uff0c\u7528\u4e8e\u56fe\u50cf\u5206\u7c7b\u4efb\u52a1\u3002<\/p>\n<\/p>\n
import tensorflow as tf<\/p>\nfrom tensorflow.keras import layers, models<\/p>\n
import numpy as np<\/p>\n
\u52a0\u8f7d\u6570\u636e\u96c6<\/strong><\/h2>\n(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()<\/p>\n
\u6570\u636e\u9884\u5904\u7406<\/strong><\/h2>\ntrain_images, test_images = train_images \/ 255.0, test_images \/ 255.0<\/p>\n
\u6784\u5efa\u6a21\u578b<\/strong><\/h2>\nmodel = models.Sequential()<\/p>\n
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))<\/p>\n
model.add(layers.MaxPooling2D((2, 2)))<\/p>\n
model.add(layers.Conv2D(64, (3, 3), activation='relu'))<\/p>\n
model.add(layers.MaxPooling2D((2, 2)))<\/p>\n
model.add(layers.Conv2D(64, (3, 3), activation='relu'))<\/p>\n
model.add(layers.Flatten())<\/p>\n
model.add(layers.Dense(64, activation='relu'))<\/p>\n
model.add(layers.Dense(10))<\/p>\n
\u7f16\u8bd1\u548c\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\nmodel.compile(optimizer='adam',<\/p>\n
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),<\/p>\n
metrics=['accuracy'])<\/p>\n
model.fit(train_images, train_labels, epochs=10, <\/p>\n
validation_data=(test_images, test_labels))<\/p>\n
\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\ntest_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)<\/p>\n
print(f'Test accuracy: {test_acc}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6587\u672c\u5206\u7c7b<\/strong><\/p>\n<\/p>\n\u6587\u672c\u5206\u7c7b\u662f\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e2d\u7684\u4e00\u4e2a\u57fa\u672c\u4efb\u52a1\uff0c\u4e3b\u8981\u7814\u7a76\u5982\u4f55\u5c06\u6587\u672c\u5206\u4e3a\u4e0d\u540c\u7684\u7c7b\u522b\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528NLTK\u548cScikit-learn\u6765\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6587\u672c\u5206\u7c7b\u6a21\u578b\uff0c\u7528\u4e8e\u5783\u573e\u90ae\u4ef6\u68c0\u6d4b\u4efb\u52a1\u3002<\/p>\n<\/p>\n
import nltk<\/p>\nfrom nltk.corpus import stopwords<\/p>\n
from sklearn.feature_extraction.text import CountVectorizer<\/p>\n
from sklearn.model_selection import train_test_split<\/p>\n
from sklearn.naive_bayes import MultinomialNB<\/p>\n
from sklearn.metrics import accuracy_score<\/p>\n
\u52a0\u8f7d\u6570\u636e\u96c6<\/strong><\/h2>\nnltk.download('stopwords')<\/p>\n
stop_words = set(stopwords.words('english'))<\/p>\n
\u6837\u672c\u6570\u636e<\/strong><\/h2>\ntexts = ["Free entry in 2 a weekly competition to win FA Cup final tickets!", "FreeMsg: Hey there darling it's been 3 week's now and no word back! I'd love some fun and good luck...",<\/p>\n
"Had your mobile 11 months or more? U R entitled to update to the latest colour mobiles with camera for Free!",<\/p>\n
"You have 1 new voicemail. Please call 1234567890"]<\/p>\n
labels = [1, 0, 1, 0] # 1\u8868\u793a\u5783\u573e\u90ae\u4ef6\uff0c0\u8868\u793a\u6b63\u5e38\u90ae\u4ef6<\/p>\n
\u6570\u636e\u9884\u5904\u7406<\/strong><\/h2>\nvectorizer = CountVectorizer(stop_words=stop_words)<\/p>\n
X = vectorizer.fit_transform(texts)<\/p>\n
y = labels<\/p>\n
\u6570\u636e\u5206\u5272<\/strong><\/h2>\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
\u6784\u5efa\u548c\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\nmodel = MultinomialNB()<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\ny_pred = model.predict(X_test)<\/p>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
print(f'Accuracy: {accuracy}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5f3a\u5316\u5b66\u4e60<\/strong><\/p>\n<\/p>\n\u5f3a\u5316\u5b66\u4e60\u662f\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u5206\u652f\uff0c\u4e3b\u8981\u7814\u7a76\u667a\u80fd\u4f53\u5982\u4f55\u901a\u8fc7\u4e0e\u73af\u5883\u7684\u4ea4\u4e92\u6765\u5b66\u4e60\u6700\u4f18\u7b56\u7565\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Gym\u548cStable-Baselines\u6765\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u5f3a\u5316\u5b66\u4e60\u6a21\u578b\uff0c\u7528\u4e8e\u73a9CartPole\u6e38\u620f\u3002<\/p>\n<\/p>\n
import gym<\/p>\nfrom stable_baselines3 import PPO<\/p>\n
\u521b\u5efa\u73af\u5883<\/strong><\/h2>\nenv = gym.make('CartPole-v1')<\/p>\n
\u6784\u5efa\u548c\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\nmodel = PPO('MlpPolicy', env, verbose=1)<\/p>\n
model.learn(total_timesteps=10000)<\/p>\n
\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\nobs = env.reset()<\/p>\n
for _ in range(1000):<\/p>\n
action, _states = model.predict(obs)<\/p>\n
obs, rewards, dones, info = env.step(action)<\/p>\n
env.render()<\/p>\n
env.close()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/p>\n<\/p>\n
Python\u5728\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u5e7f\u6cdb\u5e94\u7528\u5f97\u76ca\u4e8e\u5176\u5f3a\u5927\u7684\u5e93\u548c\u6846\u67b6\u3001\u793e\u533a\u652f\u6301\u3001\u6613\u4e8e\u5b66\u4e60\u548c\u4f7f\u7528\u3001\u4ee5\u53ca\u5e7f\u6cdb\u7684\u5e94\u7528\u9886\u57df\u3002\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u8be6\u7ec6\u63a2\u8ba8\u4e86Python\u5728\u4eba\u5de5\u667a\u80fd\u4e2d\u7684\u5e94\u7528\uff0c\u4ecb\u7ecd\u4e86\u4e00\u4e9b\u5173\u952e\u7684\u5e93\u548c\u6846\u67b6\uff0c\u5e76\u901a\u8fc7\u5177\u4f53\u7684\u5b9e\u4f8b\u5c55\u793a\u4e86Python\u5728\u4eba\u5de5\u667a\u80fd\u9886\u57df\u4e2d\u7684\u5f3a\u5927\u529f\u80fd\u3002\u65e0\u8bba\u4f60\u662f\u521d\u5b66\u8005\u8fd8\u662f\u6709\u7ecf\u9a8c\u7684\u5f00\u53d1\u8005\uff0cPython\u90fd\u662f\u4e00\u4e2a\u503c\u5f97\u5b66\u4e60\u548c\u4f7f\u7528\u7684\u7f16\u7a0b\u8bed\u8a00\u3002\u901a\u8fc7\u4e0d\u65ad\u5b66\u4e60\u548c\u5b9e\u8df5\uff0c\u4f60\u5c06\u80fd\u591f\u5145\u5206\u53d1\u6325Python\u5728\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u6f5c\u529b\uff0c\u5f00\u53d1\u51fa\u66f4\u52a0\u667a\u80fd\u548c\u9ad8\u6548\u7684\u5e94\u7528\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n Python\u5728\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u5e94\u7528\u6709\u54ea\u4e9b\uff1f<\/strong>
Python\u56e0\u5176\u7b80\u6d01\u7684\u8bed\u6cd5\u548c\u5f3a\u5927\u7684\u5e93\u652f\u6301\uff0c\u6210\u4e3a\u4eba\u5de5\u667a\u80fd\u5f00\u53d1\u7684\u9996\u9009\u8bed\u8a00\u3002\u5b83\u5e7f\u6cdb\u5e94\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u3001\u6df1\u5ea6\u5b66\u4e60\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u7b49\u9886\u57df\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecTensorFlow\u3001Keras\u3001PyTorch\u3001Scikit-learn\u548cNLTK\u7b49\uff0c\u8fd9\u4e9b\u5de5\u5177\u5e2e\u52a9\u5f00\u53d1\u8005\u8f7b\u677e\u5b9e\u73b0\u590d\u6742\u7684\u7b97\u6cd5\u548c\u6a21\u578b\u3002<\/p>\n\u5982\u4f55\u5f00\u59cb\u4f7f\u7528Python\u8fdb\u884c\u4eba\u5de5\u667a\u80fd\u5f00\u53d1\uff1f<\/strong>
\u5bf9\u4e8e\u521d\u5b66\u8005\u6765\u8bf4\uff0c\u53ef\u4ee5\u4ece\u5b66\u4e60Python\u7684\u57fa\u7840\u77e5\u8bc6\u5f00\u59cb\uff0c\u638c\u63e1\u6570\u636e\u7ed3\u6784\u3001\u63a7\u5236\u6d41\u548c\u51fd\u6570\u7b49\u57fa\u672c\u6982\u5ff5\u3002\u63a5\u4e0b\u6765\uff0c\u5efa\u8bae\u5b66\u4e60\u4e00\u4e9b\u4e13\u95e8\u7684\u4eba\u5de5\u667a\u80fd\u5e93\uff0c\u5e76\u901a\u8fc7\u5b9e\u8df5\u9879\u76ee\u6765\u5de9\u56fa\u6240\u5b66\u3002\u53c2\u4e0e\u5728\u7ebf\u8bfe\u7a0b\u3001\u9605\u8bfb\u76f8\u5173\u4e66\u7c4d\u6216\u52a0\u5165\u793e\u533a\u4e5f\u662f\u63d0\u5347\u6280\u80fd\u7684\u6709\u6548\u65b9\u5f0f\u3002<\/p>\nPython\u5728\u4eba\u5de5\u667a\u80fd\u9879\u76ee\u4e2d\u5982\u4f55\u5904\u7406\u6570\u636e\uff1f<\/strong>
\u6570\u636e\u5904\u7406\u662f\u4eba\u5de5\u667a\u80fd\u9879\u76ee\u7684\u91cd\u8981\u73af\u8282\uff0cPython\u63d0\u4f9b\u4e86\u591a\u79cd\u5f3a\u5927\u7684\u5de5\u5177\u6765\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3001\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecPandas\u7528\u4e8e\u6570\u636e\u64cd\u4f5c\uff0cNumPy\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0c\u4ee5\u53caMatplotlib\u548cSeaborn\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u3002\u8fd9\u4e9b\u5de5\u5177\u4f7f\u5f97\u5f00\u53d1\u8005\u80fd\u591f\u9ad8\u6548\u5730\u51c6\u5907\u548c\u5206\u6790\u6570\u636e\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\u548c\u6027\u80fd\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5b9e\u73b0\u4eba\u5de5\u667a\u80fd\u7684\u6838\u5fc3\u5728\u4e8e\u5176\u5f3a\u5927\u7684\u5e93\u548c\u6846\u67b6\u3001\u793e\u533a\u652f\u6301\u3001\u6613\u4e8e\u5b66\u4e60\u548c\u4f7f\u7528\u3001\u4ee5\u53ca\u5e7f\u6cdb\u7684\u5e94\u7528\u9886\u57df\u3002 \u5728\u672c\u6587\u4e2d\uff0c […]","protected":false},"author":3,"featured_media":1078737,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1078730"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1078730"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1078730\/revisions"}],"predecessor-version":[{"id":1078740,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1078730\/revisions\/1078740"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1078737"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1078730"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1078730"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1078730"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"python\u662f\u5982\u4f55\u5b9e\u73b0\u4eba\u5de5\u667a\u80fd\u7684\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24182127\/03ca0006-eac4-4b2a-9633-ba2504a1fb1e.webp\")
<\/p>\n