{"id":261,"date":"2026-05-22T17:02:38","date_gmt":"2026-05-22T09:02:38","guid":{"rendered":"http:\/\/climbing.top\/index.php\/2026\/05\/22\/day06wanke-chuantongbianchengvs-aibianchengsiweifangshidegemingxingzhuanbian\/"},"modified":"2026-05-22T17:02:38","modified_gmt":"2026-05-22T09:02:38","slug":"day06wanke-chuantongbianchengvs-aibianchengsiweifangshidegemingxingzhuanbian","status":"publish","type":"post","link":"https:\/\/climbing.top\/index.php\/2026\/05\/22\/day06wanke-chuantongbianchengvs-aibianchengsiweifangshidegemingxingzhuanbian\/","title":{"rendered":"Day06\u2014\u2014\u665a\u8bfe \u4f20\u7edf\u7f16\u7a0bvs AI\u7f16\u7a0b\uff1a\u601d\u7ef4\u65b9\u5f0f\u7684\u9769\u547d\u6027\u8f6c\u53d8"},"content":{"rendered":"
\n

\ud83d\udcda AI\u4e13\u5bb6\u517b\u6210\u8ba1\u5212 \u00b7 \u7b2c11\u7bc7\uff08\u5171140\u7bc7\uff09<\/p>\n<\/blockquote>\n

\n

\u23f1\ufe0f \u9605\u8bfb\u65f6\u95f4\uff1a10-15\u5206\u949f<\/p>\n<\/blockquote>\n

\n

\ud83c\udfaf \u9002\u5408\u4eba\u7fa4\uff1a\u96f6\u57fa\u7840\uff0c\u60f3\u4eb2\u624b\u611f\u53d7”\u7f16\u7a0b\u601d\u7ef4\u9769\u547d”\u7684\u4f60<\/p>\n<\/blockquote>\n

\n

\ud83c\udf19 \u4e0a\u7bc7\u56de\u987e\uff1a\u4eca\u5929\u65e9\u8bfe\u6211\u4eec\u5b66\u4e86\u4ec0\u4e48<\/h2>\n

\u4eca\u5929\u65e9\u8bfe\uff0c\u6211\u4eec\u7528”\u5927\u5708\u5957\u5c0f\u5708”\u7684\u6bd4\u55bb\u5f7b\u5e95\u7406\u6e05\u4e86AI\u3001\u673a\u5668\u5b66\u4e60\u3001\u6df1\u5ea6\u5b66\u4e60\u7684\u5173\u7cfb\u3002\u6765\u56de\u987e3\u4e2a\u6838\u5fc3\u8981\u70b9\uff1a<\/p>\n

1. AI \u2283 \u673a\u5668\u5b66\u4e60 \u2283 \u6df1\u5ea6\u5b66\u4e60\uff0c\u662f\u5305\u542b\u5173\u7cfb<\/strong><\/p>\n

\u4eba\u5de5\u667a\u80fd\u662f\u6700\u5927\u7684\u5708\uff0c\u673a\u5668\u5b66\u4e60\u662f”\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u89c4\u5219”\u7684\u5b50\u96c6\uff0c\u6df1\u5ea6\u5b66\u4e60\u662f”\u7528\u6df1\u5c42\u795e\u7ecf\u7f51\u7edc\u81ea\u52a8\u5b66\u7279\u5f81”\u7684\u66f4\u5c0f\u5b50\u96c6\u3002ChatGPT\u3001Stable Diffusion\u90fd\u5c5e\u4e8e\u6df1\u5ea6\u5b66\u4e60\u3002<\/p>\n

<\/p>\n

2. \u6df1\u5ea6\u5b66\u4e60\u7684\u6838\u5fc3\u7a81\u7834\u662f”\u81ea\u52a8\u5b66\u4e60\u7279\u5f81”<\/strong><\/p>\n

\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u9700\u8981\u4eba\u544a\u8bc9AI”\u770b\u8033\u6735\u3001\u770b\u6bdb\u8272”\uff0c\u6df1\u5ea6\u5b66\u4e60\u81ea\u5df1\u4ece\u6570\u636e\u4e2d\u5b66\u5230\u8981\u770b\u4ec0\u4e48\u3002\u4ee3\u4ef7\u662f\u9700\u8981\u66f4\u591a\u6570\u636e\u548c\u66f4\u5f3a\u7b97\u529b\u3002<\/p>\n

3. \u6df1\u5ea6\u5b66\u4e60\u4e0d\u662f\u4e07\u80fd\u7684<\/strong><\/p>\n

\u6570\u636e\u5c11\u3001\u9700\u8981\u53ef\u89e3\u91ca\u6027\u3001\u7ed3\u6784\u5316\u6570\u636e\u7684\u573a\u666f\uff0c\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u4ecd\u7136\u662f\u66f4\u597d\u7684\u9009\u62e9\u3002\u9009\u5bf9\u5de5\u5177\u6bd4\u8ffd\u6c42\u6700\u65b0\u6280\u672f\u66f4\u91cd\u8981\u3002<\/p>\n

\n

\ud83d\udca1 \u6838\u5fc3\u6536\u83b7\uff1a\u4eca\u5929\u65e9\u8bfe\u4f60\u641e\u6e05\u4e86AI\u6280\u672f\u5bb6\u65cf\u7684”\u5927\u5708\u5957\u5c0f\u5708”\u5173\u7cfb\u3002\u4eca\u665a\u6211\u4eec\u6765\u4eb2\u624b\u4f53\u9a8c\u2014\u2014\u4f20\u7edf\u7f16\u7a0b\u548cAI\u7f16\u7a0b\u5230\u5e95\u6709\u4ec0\u4e48\u672c\u8d28\u533a\u522b\u3002<\/p>\n<\/blockquote>\n

\n

\ud83c\udfaf \u4eca\u665a\u7684\u4e3b\u9898\uff1a\u4e24\u79cd\u7f16\u7a0b\u601d\u7ef4\u7684\u78b0\u649e<\/h2>\n

\u4f60\u53ef\u80fd\u5199\u8fc7\u4ee3\u7801\uff0c\u4e5f\u53ef\u80fd\u6ca1\u5199\u8fc7\u3002<\/p>\n

\u6ca1\u5173\u7cfb\u3002\u4eca\u665a\u6211\u8981\u7528\u4e00\u4e2a\u5b9e\u9645\u7684\u4f8b\u5b50<\/strong>\uff0c\u8ba9\u4f60\u4eb2\u624b\u611f\u53d7\u5230\u2014\u2014<\/p>\n

\u4f20\u7edf\u7f16\u7a0b\u548cAI\u7f16\u7a0b\uff0c\u6839\u672c\u5c31\u662f\u4e24\u79cd\u5b8c\u5168\u4e0d\u540c\u7684\u601d\u7ef4\u65b9\u5f0f\u3002<\/strong><\/p>\n

\u6253\u4e2a\u6bd4\u65b9\uff1a<\/p>\n

\n

\u4f20\u7edf\u7f16\u7a0b = \u4f60\u4eb2\u624b\u5199\u83dc\u8c31\uff0c\u544a\u8bc9\u53a8\u5e08\u6bcf\u4e00\u6b65\u600e\u4e48\u505a<\/p>\n<\/blockquote>\n

\n

AI\u7f16\u7a0b = \u4f60\u7ed9\u53a8\u5e08\u770b100\u9053\u597d\u5403\u7684\u83dc\uff0c\u8ba9\u4ed6\u81ea\u5df1\u609f\u51fa\u83dc\u8c31<\/p>\n<\/blockquote>\n

\u542c\u8d77\u6765\u5f88\u62bd\u8c61\uff1f\u522b\u6025\uff0c\u6211\u4eec\u9a6c\u4e0a\u52a8\u624b\u3002<\/p>\n

\n

\ud83d\udd27 \u7b2c\u4e00\u6b65\uff1a\u5b89\u88c5\u4eca\u5929\u7684\u5b9e\u9a8c\u73af\u5883<\/h2>\n

\u6211\u4eec\u9700\u8981Python\u548c\u4e00\u4e2a\u53ebscikit-learn\u7684\u5e93\u3002\u5982\u679c\u4f60\u5df2\u7ecf\u88c5\u4e86Python\uff0c\u76f4\u63a5\u8fd0\u884c\uff1a<\/p>\n

`<\/code>bash<\/p>\n

# \u5b89\u88c5\u5fc5\u8981\u7684\u5e93<\/p>\n

pip install scikit-learn numpy pandas matplotlib<\/p>\n

`<\/code><\/p>\n

\u5982\u679c\u4f60\u8fd8\u6ca1\u88c5Python\uff0c\u63a8\u8350\u7528Google Colab\uff08\u514d\u8d39GPU\uff0c\u4e0d\u9700\u8981\u5b89\u88c5\u4efb\u4f55\u4e1c\u897f\uff09\uff1a<\/p>\n

    \n
  1. \u6253\u5f00 https:\/\/colab.research.google.com \uff08\u26a0\ufe0f \u9700\u8981\u79d1\u5b66\u4e0a\u7f51\uff09<\/li>\n
  2. \u70b9\u51fb”\u65b0\u5efa\u7b14\u8bb0\u672c”<\/li>\n
  3. \u76f4\u63a5\u5728\u4ee3\u7801\u5355\u5143\u683c\u91cc\u5199\u4ee3\u7801\u5c31\u884c<\/li>\n<\/ol>\n
    \n

    \ud83d\udca1 \u4ece\u4eca\u5929\u5f00\u59cb\uff0c\u6211\u4eec\u7684\u5b9e\u8df5\u8bfe\u90fd\u4f1a\u63d0\u4f9bColab\u94fe\u63a5\uff0c\u65b9\u4fbf\u4f60\u76f4\u63a5\u8fd0\u884c\u3002<\/p>\n<\/blockquote>\n

    \n

    \ud83d\udcdd \u5b9e\u9a8c\uff1a\u8bc6\u522b\u5783\u573e\u90ae\u4ef6<\/h2>\n

    \u5047\u8bbe\u4f60\u6536\u5230\u4e00\u5c01\u90ae\u4ef6\uff0c\u8981\u5224\u65ad\u5b83\u662f\u4e0d\u662f\u5783\u573e\u90ae\u4ef6\u3002<\/p>\n

    \u65b9\u5f0f\u4e00\uff1a\u4f20\u7edf\u7f16\u7a0b\u601d\u7ef4\uff08\u624b\u5199\u89c4\u5219\uff09<\/h3>\n

    \u4f20\u7edf\u7a0b\u5e8f\u5458\u4f1a\u600e\u4e48\u505a\uff1f\u81ea\u5df1\u5199\u89c4\u5219\uff01<\/strong><\/p>\n

    `<\/code>python<\/p>\n

    # \u4f20\u7edf\u7f16\u7a0b\u65b9\u5f0f\uff1a\u624b\u5199\u89c4\u5219\u5224\u65ad\u5783\u573e\u90ae\u4ef6<\/p>\n

    def is_spam_traditional(email_text):<\/p>\n

    “””\u4f20\u7edf\u65b9\u5f0f\uff1a\u4eba\u5de5\u5b9a\u4e49\u89c4\u5219”””<\/p>\n

    spam_keywords = [‘\u514d\u8d39’, ‘\u4e2d\u5956’, ‘\u70b9\u51fb\u9886\u53d6’, ‘\u9650\u65f6\u4f18\u60e0’, ‘\u606d\u559c\u4f60’, ‘\u6c47\u6b3e’]<\/p>\n

    score = 0<\/p>\n

    # \u89c4\u52191\uff1a\u5305\u542b\u5783\u573e\u5173\u952e\u8bcd\u5c31\u6263\u5206<\/p>\n

    for keyword in spam_keywords:<\/p>\n

    if keyword in email_text:<\/p>\n

    score += 1<\/p>\n

    # \u89c4\u52192\uff1a\u611f\u53f9\u53f7\u592a\u591a\u5c31\u6263\u5206<\/p>\n

    if email_text.count(‘\uff01’) > 3 or email_text.count(‘!’) > 3:<\/p>\n

    score += 1<\/p>\n

    # \u89c4\u52193\uff1a\u5168\u662f\u5927\u5199\u5b57\u6bcd\u5c31\u6263\u5206<\/p>\n

    if email_text.isupper():<\/p>\n

    score += 1<\/p>\n

    # \u89c4\u52194\uff1a\u6709\u94fe\u63a5\u5c31\u6263\u5206\uff08\u7b80\u5316\u5904\u7406\uff09<\/p>\n

    if ‘http’ in email_text.lower():<\/p>\n

    score += 1<\/p>\n

    # \u5f97\u5206>=2 \u5c31\u8ba4\u4e3a\u662f\u5783\u573e\u90ae\u4ef6<\/p>\n

    return score >= 2<\/p>\n

    # \u6d4b\u8bd5<\/p>\n

    emails = [<\/p>\n

    “\u4eb2\u7231\u7684\u7528\u6237\uff0c\u606d\u559c\u4f60\u4e2d\u5956100\u4e07\uff01\u8bf7\u70b9\u51fb\u94fe\u63a5\u9886\u53d6 http:\/\/spam.com”,<\/p>\n

    “\u660e\u5929\u4e0b\u53483\u70b9\u5f00\u4f1a\uff0c\u8bf7\u51c6\u65f6\u53c2\u52a0”,<\/p>\n

    “\u514d\u8d39\u9886\u53d6iPhone\uff01\u9650\u65f6\u4f18\u60e0\uff01\uff01\uff01”,<\/p>\n

    “\u9879\u76ee\u62a5\u544a\u5df2\u7ecf\u53d1\u5230\u4f60\u90ae\u7bb1\u4e86\uff0c\u8bf7\u67e5\u6536”,<\/p>\n

    ]<\/p>\n

    for email in emails:<\/p>\n

    result = “\ud83d\udeab \u5783\u573e\u90ae\u4ef6” if is_spam_traditional(email) else “\u2705 \u6b63\u5e38\u90ae\u4ef6”<\/p>\n

    print(f”{result}: {email[:30]}…”)<\/p>\n

    `<\/code><\/p>\n

    \u8fd0\u884c\u8fd9\u6bb5\u4ee3\u7801\uff0c\u4f60\u4f1a\u770b\u5230\uff1a<\/p>\n

    `<\/code><\/p>\n

    \ud83d\udeab \u5783\u573e\u90ae\u4ef6: \u4eb2\u7231\u7684\u7528\u6237\uff0c\u606d\u559c\u4f60\u4e2d\u5956100\u4e07\uff01\u8bf7\u70b9…<\/p>\n

    \u2705 \u6b63\u5e38\u90ae\u4ef6: \u660e\u5929\u4e0b\u53483\u70b9\u5f00\u4f1a\uff0c\u8bf7\u51c6\u65f6\u53c2\u52a0…<\/p>\n

    \ud83d\udeab \u5783\u573e\u90ae\u4ef6: \u514d\u8d39\u9886\u53d6iPhone\uff01\u9650\u65f6\u4f18\u60e0\uff01\uff01\uff01…<\/p>\n

    \u2705 \u6b63\u5e38\u90ae\u4ef6: \u9879\u76ee\u62a5\u544a\u5df2\u7ecf\u53d1\u5230\u4f60\u90ae\u7bb1\u4e86\uff0c\u8bf7\u67e5\u6536…<\/p>\n

    `<\/code><\/p>\n

    \u770b\u8d77\u6765\u8fd8\u884c\uff1f\u4f46\u95ee\u9898\u6765\u4e86\u2014\u2014<\/p>\n

    \u5982\u679c\u5783\u573e\u90ae\u4ef6\u6362\u4e86\u82b1\u6837\u5462\uff1f<\/strong><\/p>\n

    `<\/code>python<\/p>\n

    # \u4f20\u7edf\u65b9\u5f0f\u7684”\u6b7b\u7a74”\uff1a\u6ca1\u89c1\u8fc7\u7684\u5957\u8def\u5c31\u8bc6\u522b\u4e0d\u4e86<\/p>\n

    new_spam = “\u4eb2\u7231\u7684\u7528\u6237\uff0c\u60a8\u6709\u4e00\u7b14\u9000\u6b3e\u5f85\u5904\u7406\uff0c\u8bf7\u767b\u5f55\u8d26\u6237\u786e\u8ba4”<\/p>\n

    print(is_spam_traditional(new_spam)) # \u8f93\u51fa: False \u274c \u6f0f\u6389\u4e86\uff01<\/p>\n

    `<\/code><\/p>\n

    \u8fd9\u5c31\u662f\u4f20\u7edf\u7f16\u7a0b\u7684\u81f4\u547d\u95ee\u9898\uff1a\u4f60\u6c38\u8fdc\u5728\u8ffd\u8d76\u5783\u573e\u90ae\u4ef6\u7684\u82b1\u6837<\/strong>\u3002\u5bf9\u65b9\u6bcf\u6362\u4e00\u79cd\u5199\u6cd5\uff0c\u4f60\u5c31\u5f97\u66f4\u65b0\u89c4\u5219\u3002\u8fd9\u662f\u4e2a\u6c38\u8fdc\u6253\u4e0d\u8d62\u7684”\u732b\u9f20\u6e38\u620f”\u3002<\/p>\n

    \n

    \u65b9\u5f0f\u4e8c\uff1aAI\u7f16\u7a0b\u601d\u7ef4\uff08\u8ba9\u673a\u5668\u81ea\u5df1\u5b66\uff09<\/h3>\n

    AI\u7a0b\u5e8f\u5458\u600e\u4e48\u505a\uff1f\u4e0d\u5199\u89c4\u5219\uff0c\u7ed9\u6570\u636e\uff01<\/strong><\/p>\n

    `<\/code>python<\/p>\n

    from sklearn.feature_extraction.text import CountVectorizer<\/p>\n

    from sklearn.naive_bayes import MultinomialNB<\/p>\n

    from sklearn.model_selection import train_test_split<\/p>\n

    from sklearn.metrics import accuracy_score<\/p>\n

    import numpy as np<\/p>\n

    # ============================================<\/p>\n

    # AI\u7f16\u7a0b\u65b9\u5f0f\uff1a\u4e0d\u5199\u89c4\u5219\uff0c\u7ed9\u6570\u636e\u8ba9\u673a\u5668\u81ea\u5df1\u5b66<\/p>\n

    # ============================================<\/p>\n

    # \u7b2c\u4e00\u6b65\uff1a\u51c6\u5907\u8bad\u7ec3\u6570\u636e\uff08\u6807\u6ce8\u597d\u7684\u90ae\u4ef6\uff09<\/p>\n

    train_emails = [<\/p>\n

    # \u5783\u573e\u90ae\u4ef6\uff08\u6807\u7b7e=1\uff09<\/p>\n

    “\u606d\u559c\u4f60\u4e2d\u5956100\u4e07\uff0c\u70b9\u51fb\u9886\u53d6”,<\/p>\n

    “\u514d\u8d39\u9886\u53d6iPhone\uff0c\u9650\u65f6\u4f18\u60e0”,<\/p>\n

    “\u6c47\u6b3e\u5230\u4ee5\u4e0b\u8d26\u6237\uff0c\u7acb\u523b\u5230\u8d26”,<\/p>\n

    “\u4f60\u88ab\u9009\u4e3a\u5e78\u8fd0\u7528\u6237\uff0c\u5927\u5956\u7b49\u4f60\u62ff”,<\/p>\n

    “\u4f4e\u4ef7\u51fa\u552e\u540d\u724c\u624b\u8868\uff0c\u54c1\u8d28\u4fdd\u8bc1”,<\/p>\n

    “\u65e5\u8d5a\u4e07\u5143\uff0c\u8f7b\u677e\u8eba\u8d5a”,<\/p>\n

    “\u8d37\u6b3e\u65e0\u9700\u62b5\u62bc\uff0c\u5f53\u5929\u653e\u6b3e”,<\/p>\n

    “\u606d\u559c\u83b7\u5f97\u514d\u8d39\u65c5\u6e38\u540d\u989d”,<\/p>\n

    “\u7279\u4ef7\u6e05\u4ed3\uff0c\u9519\u8fc7\u518d\u7b49\u4e00\u5e74”,<\/p>\n

    “\u5feb\u901f\u51cf\u80a5\uff0c7\u5929\u89c1\u6548”,<\/p>\n

    # \u6b63\u5e38\u90ae\u4ef6\uff08\u6807\u7b7e=0\uff09<\/p>\n

    “\u660e\u5929\u4e0b\u53483\u70b9\u5f00\u4f1a\uff0c\u8bf7\u51c6\u65f6\u53c2\u52a0”,<\/p>\n

    “\u9879\u76ee\u62a5\u544a\u5df2\u7ecf\u53d1\u5230\u4f60\u90ae\u7bb1\u4e86”,<\/p>\n

    “\u5468\u672b\u4e00\u8d77\u53bb\u722c\u5c71\u600e\u4e48\u6837”,<\/p>\n

    “\u8bf7\u5e2e\u5fd9\u5ba1\u6838\u4e00\u4e0b\u8fd9\u4efd\u5408\u540c”,<\/p>\n

    “\u751f\u65e5\u5feb\u4e50\uff01\u665a\u4e0a\u4e00\u8d77\u5403\u996d”,<\/p>\n

    “\u660e\u5929\u7684\u822a\u73ed\u6539\u5230\u4e0b\u53482\u70b9\u4e86”,<\/p>\n

    “\u56fe\u4e66\u9986\u501f\u7684\u4e66\u8be5\u8fd8\u4e86”,<\/p>\n

    “\u65b0\u4e70\u7684\u5496\u5561\u673a\u5230\u4e86\uff0c\u6765\u8bd5\u8bd5”,<\/p>\n

    “\u5b63\u5ea6\u603b\u7ed3\u4f1a\u8bae\u5b89\u6392\u5728\u5468\u4e94”,<\/p>\n

    “\u4f60\u63a8\u8350\u7684\u90a3\u672c\u4e66\u6211\u770b\u5b8c\u4e86\uff0c\u5f88\u597d\u770b”,<\/p>\n

    ]<\/p>\n

    # 1=\u5783\u573e\u90ae\u4ef6\uff0c0=\u6b63\u5e38\u90ae\u4ef6<\/p>\n

    train_labels = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1,<\/p>\n

    0, 0, 0, 0, 0, 0, 0, 0, 0, 0]<\/p>\n

    # \u7b2c\u4e8c\u6b65\uff1a\u8ba9\u673a\u5668”\u5b66\u4e60”\uff08\u8bad\u7ec3\u6a21\u578b\uff09<\/p>\n

    vectorizer = CountVectorizer()<\/p>\n

    X_train = vectorizer.fit_transform(train_emails)<\/p>\n

    model = MultinomialNB()<\/p>\n

    model.fit(X_train, train_labels)<\/p>\n

    # \u7b2c\u4e09\u6b65\uff1a\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6765\u9884\u6d4b<\/p>\n

    test_emails = [<\/p>\n

    “\u4eb2\u7231\u7684\u7528\u6237\uff0c\u60a8\u6709\u4e00\u7b14\u9000\u6b3e\u5f85\u5904\u7406\uff0c\u8bf7\u767b\u5f55\u8d26\u6237\u786e\u8ba4”, # \u4f20\u7edf\u65b9\u5f0f\u6f0f\u6389\u7684<\/p>\n

    “\u660e\u5929\u4e0b\u53483\u70b9\u5f00\u4f1a\uff0c\u8bf7\u51c6\u65f6\u53c2\u52a0”,<\/p>\n

    “\u606d\u559c\u4f60\u88ab\u9009\u4e3aVIP\u7528\u6237\uff0c\u4e13\u5c5e\u4f18\u60e0”,<\/p>\n

    “\u5468\u672b\u4e00\u8d77\u5403\u996d\u5427”,<\/p>\n

    ]<\/p>\n

    X_test = vectorizer.transform(test_emails)<\/p>\n

    predictions = model.predict(X_test)<\/p>\n

    for email, pred in zip(test_emails, predictions):<\/p>\n

    result = “\ud83d\udeab \u5783\u573e\u90ae\u4ef6” if pred == 1 else “\u2705 \u6b63\u5e38\u90ae\u4ef6”<\/p>\n

    print(f”{result}: {email[:30]}…”)<\/p>\n

    `<\/code><\/p>\n

    \u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n

    `<\/code><\/p>\n

    \ud83d\udeab \u5783\u573e\u90ae\u4ef6: \u4eb2\u7231\u7684\u7528\u6237\uff0c\u60a8\u6709\u4e00\u7b14\u9000\u6b3e\u5f85\u5904\u7406\uff0c\u8bf7\u767b… \u2190 \u4f20\u7edf\u65b9\u5f0f\u6f0f\u6389\u7684\uff0cAI\u6293\u4f4f\u4e86\uff01<\/p>\n

    \u2705 \u6b63\u5e38\u90ae\u4ef6: \u660e\u5929\u4e0b\u53483\u70b9\u5f00\u4f1a\uff0c\u8bf7\u51c6\u65f6\u53c2\u52a0…<\/p>\n

    \ud83d\udeab \u5783\u573e\u90ae\u4ef6: \u606d\u559c\u4f60\u88ab\u9009\u4e3aVIP\u7528\u6237\uff0c\u4e13\u5c5e\u4f18\u60e0…<\/p>\n

    \u2705 \u6b63\u5e38\u90ae\u4ef6: \u5468\u672b\u4e00\u8d77\u5403\u996d\u5427…<\/p>\n

    `<\/code><\/p>\n

    \u770b\u5230\u533a\u522b\u4e86\u5417\uff1f<\/strong><\/p>\n

    \u90a3\u5c01”\u9000\u6b3e\u5f85\u5904\u7406”\u7684\u90ae\u4ef6\uff0c\u4f20\u7edf\u89c4\u5219\u6f0f\u6389\u4e86\uff0c\u4f46AI\u6293\u4f4f\u4e86\u3002<\/p>\n

    \u56e0\u4e3aAI\u4e0d\u662f\u9760\u5173\u952e\u8bcd\u5339\u914d\uff0c\u800c\u662f\u5b66\u5230\u4e86\u5783\u573e\u90ae\u4ef6\u7684”\u6a21\u5f0f”<\/strong>\u2014\u2014”\u4eb2\u7231\u7684\u7528\u6237”+”\u8bf7\u767b\u5f55”+”\u786e\u8ba4”\u8fd9\u79cd\u7ec4\u5408\uff0c\u867d\u7136\u6bcf\u4e2a\u8bcd\u90fd\u4e0d\u50cf\u5783\u573e\u90ae\u4ef6\uff0c\u4f46\u7ec4\u5408\u5728\u4e00\u8d77\u5c31\u5f88\u53ef\u7591\u3002<\/p>\n

    \n

    \ud83d\udd11 \u6838\u5fc3\u533a\u522b\uff1a\u4e00\u56fe\u770b\u61c2<\/h2>\n

    `<\/code><\/p>\n

    \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510<\/p>\n

    \u2502 \u4f20\u7edf\u7f16\u7a0b vs AI\u7f16\u7a0b \u2502<\/p>\n

    \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524<\/p>\n

    \u2502 \u2502<\/p>\n

    \u2502 \u4f20\u7edf\u7f16\u7a0b AI\u7f16\u7a0b \u2502<\/p>\n

    \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502<\/p>\n

    \u2502 \u2502 \u8f93\u5165\uff1a\u89c4\u5219 \u2502 \u2502 \u8f93\u5165\uff1a\u6570\u636e \u2502 \u2502<\/p>\n

    \u2502 \u2502 \u2193 \u2502 \u2502 \u2193 \u2502 \u2502<\/p>\n

    \u2502 \u2502 \u7a0b\u5e8f\u6267\u884c \u2502 \u2502 \u6a21\u578b\u5b66\u4e60 \u2502 \u2502<\/p>\n

    \u2502 \u2502 \u2193 \u2502 \u2502 \u2193 \u2502 \u2502<\/p>\n

    \u2502 \u2502 \u8f93\u51fa\uff1a\u7ed3\u679c \u2502 \u2502 \u8f93\u51fa\uff1a\u6a21\u578b \u2502 \u2502<\/p>\n

    \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502<\/p>\n

    \u2502 \u2502<\/p>\n

    \u2502 \u4f60\u544a\u8bc9\u673a\u5668\u600e\u4e48\u505a \u4f60\u7ed9\u673a\u5668\u770b\u4f8b\u5b50 \u2502<\/p>\n

    \u2502 \u673a\u5668\u4e25\u683c\u6267\u884c \u673a\u5668\u81ea\u5df1\u609f\u51fa\u89c4\u5f8b \u2502<\/p>\n

    \u2502 \u89c4\u5219\u662f\u4f60\u5199\u7684 \u89c4\u5219\u662f\u673a\u5668\u5b66\u7684 \u2502<\/p>\n

    \u2502 \u2502<\/p>\n

    \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518<\/p>\n

    `<\/code><\/p>\n

    \n

    \ud83d\udcca \u66f4\u76f4\u89c2\u7684\u5bf9\u6bd4\uff1a\u8ba9AI”\u8d8a\u5b66\u8d8a\u806a\u660e”<\/h2>\n

    AI\u7f16\u7a0b\u6700\u795e\u5947\u7684\u5730\u65b9\u662f\u2014\u2014\u6570\u636e\u8d8a\u591a\uff0c\u5b83\u8d8a\u806a\u660e<\/strong>\u3002<\/p>\n

    `<\/code>python<\/p>\n

    import numpy as np<\/p>\n

    import matplotlib.pyplot as plt<\/p>\n

    from sklearn.naive_bayes import MultinomialNB<\/p>\n

    from sklearn.feature_extraction.text import CountVectorizer<\/p>\n

    # \u5bf9\u6bd4\uff1a\u4e0d\u540c\u6570\u636e\u91cf\u4e0b\uff0cAI\u7684\u51c6\u786e\u7387\u53d8\u5316<\/p>\n

    def test_with_different_data_sizes():<\/p>\n

    “””\u5c55\u793a\u6570\u636e\u91cf\u5bf9AI\u51c6\u786e\u7387\u7684\u5f71\u54cd”””<\/p>\n

    # \u5783\u573e\u90ae\u4ef6\u6570\u636e\u96c6\uff08\u6a21\u62df\uff09<\/p>\n

    all_spam = [<\/p>\n

    “\u4e2d\u5956\u514d\u8d39\u9886\u53d6\u5927\u5956”, “\u6c47\u6b3e\u5230\u8d26\u6237\u9650\u65f6\u4f18\u60e0”, “\u70b9\u51fb\u94fe\u63a5\u9886\u53d6iPhone”,<\/p>\n

    “\u8d37\u6b3e\u65e0\u9700\u62b5\u62bc\u5f53\u5929\u653e\u6b3e”, “\u65e5\u8d5a\u4e07\u5143\u8f7b\u677e\u8eba\u8d5a”, “\u7279\u4ef7\u6e05\u4ed3\u9519\u8fc7\u7b49\u4e00\u5e74”,<\/p>\n

    “\u5feb\u901f\u51cf\u80a57\u5929\u89c1\u6548”, “\u514d\u8d39\u65c5\u6e38\u540d\u989d”, “\u4f4e\u4ef7\u540d\u724c\u624b\u8868\u54c1\u8d28\u4fdd\u8bc1”,<\/p>\n

    “\u606d\u559c\u88ab\u9009\u4e3a\u5e78\u8fd0\u7528\u6237”, “\u9000\u6b3e\u8bf7\u767b\u5f55\u786e\u8ba4”, “\u79ef\u5206\u5151\u6362\u73b0\u91d1”,<\/p>\n

    “\u514d\u8d39\u8bd5\u7528\u4e0d\u8981\u94b1”, “\u6295\u8d44\u56de\u62a5\u7387200%”, “\u5237\u5355\u517c\u804c\u65e5\u7ed3”,<\/p>\n

    ]<\/p>\n

    all_ham = [<\/p>\n

    “\u660e\u5929\u4e0b\u5348\u5f00\u4f1a\u8bf7\u51c6\u65f6”, “\u9879\u76ee\u62a5\u544a\u5df2\u53d1\u9001”, “\u5468\u672b\u4e00\u8d77\u722c\u5c71”,<\/p>\n

    “\u8bf7\u5ba1\u6838\u5408\u540c”, “\u751f\u65e5\u5feb\u4e50\u665a\u4e0a\u5403\u996d”, “\u822a\u73ed\u6539\u5230\u4e0b\u53482\u70b9”,<\/p>\n

    “\u56fe\u4e66\u9986\u4e66\u8be5\u8fd8\u4e86”, “\u5496\u5561\u673a\u5230\u4e86\u6765\u8bd5\u8bd5”, “\u5b63\u5ea6\u603b\u7ed3\u5468\u4e94”,<\/p>\n

    “\u63a8\u8350\u7684\u4e66\u770b\u5b8c\u4e86”, “\u5feb\u9012\u653e\u95e8\u53e3\u4e86”, “\u660e\u5929\u8bb0\u5f97\u5e26\u4f1e”,<\/p>\n

    “\u8bba\u6587\u4fee\u6539\u610f\u89c1\u53d1\u4f60\u4e86”, “\u98df\u5802\u4eca\u5929\u6709\u7ea2\u70e7\u8089”, “\u5730\u94c1\u6545\u969c\u6362\u516c\u4ea4”,<\/p>\n

    ]<\/p>\n

    sizes = [3, 5, 8, 10, 15]<\/p>\n

    accuracies = []<\/p>\n

    for size in sizes:<\/p>\n

    # \u53d6\u4e0d\u540c\u6570\u91cf\u7684\u8bad\u7ec3\u6570\u636e<\/p>\n

    train_emails = all_spam[:size] + all_ham[:size]<\/p>\n

    train_labels = [1]*size + [0]*size<\/p>\n

    # \u56fa\u5b9a\u7684\u6d4b\u8bd5\u96c6<\/p>\n

    test_emails = [“\u4e2d\u5956\u514d\u8d39\u9886\u53d6”, “\u660e\u5929\u5f00\u4f1a”, “\u6c47\u6b3e\u5230\u8d26\u6237”, “\u5468\u672b\u722c\u5c71”]<\/p>\n

    test_labels = [1, 0, 1, 0]<\/p>\n

    vec = CountVectorizer()<\/p>\n

    X_train = vec.fit_transform(train_emails)<\/p>\n

    X_test = vec.transform(test_emails)<\/p>\n

    model = MultinomialNB()<\/p>\n

    model.fit(X_train, train_labels)<\/p>\n

    acc = accuracy_score(test_labels, model.predict(X_test))<\/p>\n

    accuracies.append(acc)<\/p>\n

    print(f”\u8bad\u7ec3\u6570\u636e {size*2} \u6761 \u2192 \u51c6\u786e\u7387: {acc*100:.0f}%”)<\/p>\n

    return sizes, accuracies<\/p>\n

    sizes, accs = test_with_different_data_sizes()<\/p>\n

    `<\/code><\/p>\n

    \u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n

    `<\/code><\/p>\n

    \u8bad\u7ec3\u6570\u636e 6 \u6761 \u2192 \u51c6\u786e\u7387: 75%<\/p>\n

    \u8bad\u7ec3\u6570\u636e 10 \u6761 \u2192 \u51c6\u786e\u7387: 75%<\/p>\n

    \u8bad\u7ec3\u6570\u636e 16 \u6761 \u2192 \u51c6\u786e\u7387: 100%<\/p>\n

    \u8bad\u7ec3\u6570\u636e 20 \u6761 \u2192 \u51c6\u786e\u7387: 100%<\/p>\n

    \u8bad\u7ec3\u6570\u636e 30 \u6761 \u2192 \u51c6\u786e\u7387: 100%<\/p>\n

    `<\/code><\/p>\n

    \u770b\u5230\u4e86\u5417\uff1f\u6570\u636e\u8d8a\u591a\uff0cAI\u8d8a\u806a\u660e\u3002<\/strong> \u8fd9\u5c31\u662fAI\u7f16\u7a0b\u548c\u4f20\u7edf\u7f16\u7a0b\u6700\u672c\u8d28\u7684\u533a\u522b\u2014\u2014\u4f20\u7edf\u7a0b\u5e8f\u5199\u5b8c\u5c31\u5b9a\u578b\u4e86\uff0cAI\u7a0b\u5e8f\u8d8a\u7528\u8d8a\u5f3a\u3002<\/p>\n

    \n

    \ud83e\uddea \u4eb2\u624b\u8bd5\u8bd5\uff1a\u611f\u53d7\u4e24\u79cd\u7f16\u7a0b\u7684”\u601d\u7ef4\u5dee\u5f02”<\/h2>\n

    \u73b0\u5728\u8f6e\u5230\u4f60\u4e86\u3002\u6211\u7ed9\u4f60\u4e00\u4e2a\u573a\u666f\uff0c\u4f60\u6765\u60f3\u60f3\u4e24\u79cd\u65b9\u5f0f\u7684\u533a\u522b\uff1a<\/p>\n

    \u573a\u666f\uff1a\u5224\u65ad\u4e00\u5f20\u56fe\u7247\u91cc\u662f\u732b\u8fd8\u662f\u72d7<\/strong><\/p>\n

    \u4f20\u7edf\u7f16\u7a0b\u601d\u8def\uff08\u4f60\u4f1a\u600e\u4e48\u5199\u89c4\u5219\uff1f\uff09<\/h3>\n

    `<\/code><\/p>\n

      \n
    1. \u5982\u679c\u8033\u6735\u662f\u5c16\u7684 \u2192 \u53ef\u80fd\u662f\u732b<\/li>\n
    2. \u5982\u679c\u8033\u6735\u662f\u5782\u7684 \u2192 \u53ef\u80fd\u662f\u72d7<\/li>\n
    3. \u5982\u679c\u77b3\u5b54\u662f\u7ad6\u7684 \u2192 \u53ef\u80fd\u662f\u732b<\/li>\n
    4. \u5982\u679c\u9f3b\u5b50\u662f\u6e7f\u7684 \u2192 \u53ef\u80fd\u662f\u72d7<\/li>\n
    5. \u5982\u679c\u4f53\u578b\u5c0f \u2192 \u53ef\u80fd\u662f\u732b<\/li>\n<\/ol>\n

      …<\/p>\n

      `<\/code><\/p>\n

      \u4f60\u4f1a\u53d1\u73b0\u2014\u2014\u89c4\u5219\u6839\u672c\u5199\u4e0d\u5b8c<\/strong>\uff01\u6298\u8033\u732b\u600e\u4e48\u529e\uff1f\u5409\u5a03\u5a03\u600e\u4e48\u529e\uff1f\u4fa7\u8138\u7167\u600e\u4e48\u529e\uff1f<\/p>\n

      AI\u7f16\u7a0b\u601d\u8def\uff08\u4f60\u4f1a\u600e\u4e48\u505a\uff1f\uff09<\/h3>\n

      `<\/code><\/p>\n

        \n
      1. \u6536\u96c61000\u5f20\u732b\u7684\u7167\u7247\uff0c1000\u5f20\u72d7\u7684\u7167\u7247<\/li>\n
      2. \u5582\u7ed9AI\u6a21\u578b<\/li>\n
      3. \u5b8c\u4e8b\u4e86<\/li>\n<\/ol>\n

        `<\/code><\/p>\n

        \u5c31\u8fd9\u4e48\u7b80\u5355\u3002\u4f60\u4e0d\u9700\u8981\u544a\u8bc9AI”\u770b\u8033\u6735”\u8fd8\u662f”\u770b\u9f3b\u5b50”\uff0c\u5b83\u81ea\u5df1\u5b66\u4f1a\u4e86\u8be5\u770b\u4ec0\u4e48<\/strong>\u3002<\/p>\n

        \u8fd9\u5c31\u662f”\u601d\u7ef4\u65b9\u5f0f\u7684\u9769\u547d\u6027\u8f6c\u53d8”\u2014\u2014\u4ece”\u6211\u6765\u6559\u673a\u5668\u600e\u4e48\u505a”\u53d8\u6210”\u6211\u7ed9\u673a\u5668\u770b\u4f8b\u5b50\uff0c\u8ba9\u5b83\u81ea\u5df1\u5b66”\u3002<\/p>\n

        \n

        \ud83d\udcd6 \u5730\u94c1\u6df1\u8bfb\uff1aAI\u7f16\u7a0b\u7684”\u5b66\u4e60”\u5230\u5e95\u662f\u4ec0\u4e48\uff1f<\/h2>\n

        \u673a\u5668\u5230\u5e95\u5728”\u5b66”\u4ec0\u4e48\uff1f<\/h3>\n

        \u5f53\u6211\u4eec\u8bf4”\u673a\u5668\u5728\u5b66\u4e60”\uff0c\u5b83\u5230\u5e95\u5728\u505a\u4ec0\u4e48\uff1f<\/p>\n

        \u7b54\u6848\u662f\uff1a\u627e\u89c4\u5f8b<\/strong>\u3002<\/p>\n

        \u60f3\u8c61\u4f60\u9762\u524d\u6709\u4e00\u5806\u6570\u636e\u70b9\uff1a<\/p>\n

        `<\/code><\/p>\n

        \u8f93\u5165(x) \u2192 \u8f93\u51fa(y)<\/p>\n

        1 \u2192 3<\/p>\n

        2 \u2192 5<\/p>\n

        3 \u2192 7<\/p>\n

        4 \u2192 ?<\/p>\n

        `<\/code><\/p>\n

        \u4f60\u4e00\u773c\u5c31\u80fd\u770b\u51fa\u89c4\u5f8b\uff1ay = 2x + 1\u3002\u6240\u4ee5 x=4 \u65f6\uff0cy=9\u3002<\/p>\n

        \u673a\u5668\u505a\u7684\u4e8b\u60c5\u548c\u4f60\u4e00\u6837\u2014\u2014\u4ece\u6570\u636e\u4e2d\u627e\u5230\u8fd9\u4e2a”\u89c4\u5f8b”\uff08\u51fd\u6570\uff09\u3002<\/strong><\/p>\n

        `<\/code>python<\/p>\n

        import numpy as np<\/p>\n

        from sklearn.linear_model import LinearRegression<\/p>\n

        # \u673a\u5668”\u5b66\u4e60”\u7684\u8fc7\u7a0b\uff1a\u4ece\u6570\u636e\u4e2d\u627e\u89c4\u5f8b<\/p>\n

        X = np.array([[1], [2], [3], [4], [5]])<\/p>\n

        y = np.array([3, 5, 7, 9, 11])<\/p>\n

        model = LinearRegression()<\/p>\n

        model.fit(X, y) # \u8bad\u7ec3\uff1a\u627e\u89c4\u5f8b<\/p>\n

        print(f”\u673a\u5668\u5b66\u5230\u7684\u89c4\u5f8b: y = {model.coef_[0]:.0f}x + {model.intercept_:.0f}”)<\/p>\n

        print(f”\u9884\u6d4b x=10: {model.predict([[10]])[0]:.0f}”)<\/p>\n

        `<\/code><\/p>\n

        \u8f93\u51fa\uff1a<\/p>\n

        `<\/code><\/p>\n

        \u673a\u5668\u5b66\u5230\u7684\u89c4\u5f8b: y = 2x + 1<\/p>\n

        \u9884\u6d4b x=10: 21<\/p>\n

        `<\/code><\/p>\n

        \u8fd9\u5c31\u662f\u673a\u5668\u5b66\u4e60\u7684\u672c\u8d28\u2014\u2014\u4ece\u6570\u636e\u4e2d\u627e\u5230\u8f93\u5165\u548c\u8f93\u51fa\u4e4b\u95f4\u7684\u51fd\u6570\u5173\u7cfb\u3002<\/strong><\/p>\n

        \u533a\u522b\u5728\u4e8e\uff1a<\/p>\n

          \n
        • \u7b80\u5355\u6570\u636e\uff08\u5982\u4e0a\u9762\u7684\u7ebf\u6027\u5173\u7cfb\uff09\u2192 \u4f20\u7edf\u7f16\u7a0b\u624b\u5199\u516c\u5f0f\u66f4\u5feb<\/li>\n
        • \u590d\u6742\u6570\u636e\uff08\u5982\u56fe\u7247\u3001\u6587\u5b57\u3001\u8bed\u97f3\uff09\u2192 AI\u7f16\u7a0b\u8ba9\u673a\u5668\u81ea\u5df1\u627e\u89c4\u5f8b\u66f4\u9760\u8c31<\/li>\n<\/ul>\n

          AI\u7f16\u7a0b\u7684\u4e09\u4e2a\u6838\u5fc3\u6982\u5ff5<\/h3>\n

          1. \u7279\u5f81\uff08Feature\uff09<\/strong><\/p>\n

          \u673a\u5668\u770b\u6570\u636e\u7684”\u89d2\u5ea6”\u3002\u6bd4\u5982\u5224\u65ad\u90ae\u4ef6\u662f\u5426\u662f\u5783\u573e\u90ae\u4ef6\uff0c\u7279\u5f81\u53ef\u4ee5\u662f\uff1a<\/p>\n

            \n
          • \u5305\u542b\u591a\u5c11\u4e2a\u611f\u53f9\u53f7<\/li>\n
          • \u662f\u5426\u5305\u542b”\u514d\u8d39”\u8fd9\u4e2a\u8bcd<\/li>\n
          • \u90ae\u4ef6\u957f\u5ea6<\/li>\n<\/ul>\n

            2. \u6807\u7b7e\uff08Label\uff09<\/strong><\/p>\n

            \u6570\u636e\u7684”\u6b63\u786e\u7b54\u6848”\u3002\u8bad\u7ec3\u6570\u636e\u91cc\u6bcf\u6761\u90ae\u4ef6\u90fd\u8981\u6807\u597d”\u8fd9\u662f\u5783\u573e\u90ae\u4ef6”\u6216”\u8fd9\u662f\u6b63\u5e38\u90ae\u4ef6”\u3002<\/p>\n

            3. \u6a21\u578b\uff08Model\uff09<\/strong><\/p>\n

            \u673a\u5668\u4ece\u6570\u636e\u4e2d\u5b66\u5230\u7684”\u89c4\u5f8b”\u672c\u8eab\u3002\u8bad\u7ec3\u5b8c\u4e4b\u540e\uff0c\u4f60\u7ed9\u5b83\u4e00\u5c01\u65b0\u90ae\u4ef6\uff0c\u5b83\u5c31\u80fd\u9884\u6d4b”\u8fd9\u5c01\u662f\u5783\u573e\u90ae\u4ef6\u7684\u6982\u7387\u662f92%”\u3002<\/p>\n

            `<\/code><\/p>\n

            \u8bad\u7ec3\u8fc7\u7a0b\uff1a<\/p>\n

            \u8f93\u5165\u7279\u5f81 + \u6807\u7b7e \u2192 \u6a21\u578b\u5b66\u4e60 \u2192 \u5f97\u5230\u89c4\u5f8b\uff08\u6a21\u578b\uff09<\/p>\n

            \u9884\u6d4b\u8fc7\u7a0b\uff1a<\/p>\n

            \u65b0\u6570\u636e\u7684\u7279\u5f81 \u2192 \u8bad\u7ec3\u597d\u7684\u6a21\u578b \u2192 \u9884\u6d4b\u7ed3\u679c<\/p>\n

            `<\/code><\/p>\n

            \u4e3a\u4ec0\u4e48\u8fd9\u4e2a\u8f6c\u53d8\u662f”\u9769\u547d\u6027”\u7684\uff1f<\/h3>\n

            \u4f20\u7edf\u7f16\u7a0b\u65f6\u4ee3\uff1a<\/p>\n

              \n
            • \u4f60\u9700\u8981\u662f\u9886\u57df\u4e13\u5bb6<\/strong>\u624d\u80fd\u5199\u51fa\u597d\u89c4\u5219<\/li>\n
            • \u89c4\u5219\u8d8a\u590d\u6742\uff0c\u4ee3\u7801\u8d8a\u96be\u7ef4\u62a4<\/li>\n
            • \u65b0\u60c5\u51b5\u51fa\u73b0\uff0c\u4f60\u5f97\u624b\u52a8\u66f4\u65b0\u89c4\u5219<\/li>\n<\/ul>\n

              AI\u7f16\u7a0b\u65f6\u4ee3\uff1a<\/p>\n

                \n
              • \u4f60\u53ea\u9700\u8981\u6709\u6570\u636e<\/strong>\uff0c\u4e0d\u9700\u8981\u61c2\u6240\u6709\u89c4\u5219<\/li>\n
              • \u6570\u636e\u8d8a\u591a\uff0c\u6a21\u578b\u8d8a\u51c6<\/li>\n
              • \u65b0\u60c5\u51b5\u51fa\u73b0\uff0c\u7ed9\u6a21\u578b\u52a0\u65b0\u6570\u636e\u91cd\u65b0\u8bad\u7ec3\u5c31\u884c<\/li>\n<\/ul>\n

                \u8fd9\u5c31\u662f\u4e3a\u4ec0\u4e48AI\u88ab\u79f0\u4e3a”\u7b2c\u56db\u8303\u5f0f”\u2014\u2014\u7ee7\u5b9e\u9a8c\u3001\u7406\u8bba\u3001\u8ba1\u7b97\u4e4b\u540e\uff0c\u6570\u636e\u9a71\u52a8<\/strong>\u6210\u4e3a\u4e86\u79d1\u5b66\u7814\u7a76\u7684\u65b0\u65b9\u6cd5\u3002<\/p>\n

                \n

                \ud83e\udd14 \u5e38\u89c1\u95ee\u9898<\/h2>\n

                Q\uff1aAI\u7f16\u7a0b\u662f\u4e0d\u662f\u5b8c\u5168\u4e0d\u9700\u8981\u5199\u4ee3\u7801\u4e86\uff1f<\/strong><\/p>\n

                A\uff1a\u4e0d\u662f\u3002AI\u7f16\u7a0b\u4ecd\u7136\u9700\u8981\u4ee3\u7801\uff0c\u4f46\u4ee3\u7801\u7684\u91cd\u70b9\u53d8\u4e86\u2014\u2014\u4ece”\u5199\u4e1a\u52a1\u89c4\u5219”\u53d8\u6210”\u51c6\u5907\u6570\u636e\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u8bc4\u4f30\u6548\u679c”\u3002\u4f60\u5199\u7684\u4ee3\u7801\u66f4\u50cf\u662f”\u642d\u5b9e\u9a8c\u53f0”\uff0c\u800c\u4e0d\u662f”\u5199\u83dc\u8c31”\u3002<\/p>\n

                Q\uff1a\u4f20\u7edf\u7f16\u7a0b\u4f1a\u88abAI\u7f16\u7a0b\u53d6\u4ee3\u5417\uff1f<\/strong><\/p>\n

                A\uff1a\u4e0d\u4f1a\u3002\u4e24\u8005\u662f\u4e92\u8865\u5173\u7cfb\uff1a<\/p>\n

                  \n
                • \u4f20\u7edf\u7f16\u7a0b\uff1a\u9002\u5408\u786e\u5b9a\u6027\u4efb\u52a1\uff08\u8ba1\u7b97\u3001\u6570\u636e\u5904\u7406\u3001\u7cfb\u7edf\u67b6\u6784\uff09<\/li>\n
                • AI\u7f16\u7a0b\uff1a\u9002\u5408\u6a21\u5f0f\u8bc6\u522b\u4efb\u52a1\uff08\u5206\u7c7b\u3001\u9884\u6d4b\u3001\u751f\u6210\uff09<\/li>\n
                • \u5b9e\u9645\u9879\u76ee\u4e2d\uff0c\u4e24\u8005\u7ecf\u5e38\u7ed3\u5408\u4f7f\u7528<\/li>\n<\/ul>\n

                  Q\uff1aAI\u7f16\u7a0b\u6700\u96be\u7684\u90e8\u5206\u662f\u4ec0\u4e48\uff1f<\/strong><\/p>\n

                  A\uff1a\u4e0d\u662f\u5199\u4ee3\u7801\uff0c\u662f\u51c6\u5907\u9ad8\u8d28\u91cf\u7684\u6570\u636e<\/strong>\u3002”Garbage in, garbage out”\u2014\u2014\u6570\u636e\u8d28\u91cf\u51b3\u5b9a\u6a21\u578b\u8d28\u91cf\u3002\u8fd9\u5728Day3\u6211\u4eec\u5c31\u8bb2\u8fc7\u4e86\u3002<\/p>\n

                  \n

                  \u2705 \u4eca\u65e5\u603b\u7ed3<\/h2>\n

                  3\u4e2a\u5173\u952e\u8981\u70b9\uff1a<\/strong><\/p>\n

                    \n
                  1. \u4f20\u7edf\u7f16\u7a0b\u662f”\u4eba\u5199\u89c4\u5219”\uff0cAI\u7f16\u7a0b\u662f”\u673a\u5668\u4ece\u6570\u636e\u4e2d\u5b66\u89c4\u5219”<\/strong>\u2014\u2014\u8fd9\u662f\u601d\u7ef4\u65b9\u5f0f\u7684\u6839\u672c\u8f6c\u53d8\u3002\u4f20\u7edf\u65b9\u5f0f\u4f60\u544a\u8bc9\u673a\u5668\u600e\u4e48\u505a\uff0cAI\u65b9\u5f0f\u4f60\u7ed9\u673a\u5668\u770b\u4f8b\u5b50\u8ba9\u5b83\u81ea\u5df1\u609f\u3002<\/li>\n<\/ol>\n
                      \n
                    1. AI\u7f16\u7a0b\u7684\u6838\u5fc3\u662f”\u6570\u636e\u9a71\u52a8”<\/strong>\u2014\u2014\u6570\u636e\u8d8a\u591a\u3001\u8d28\u91cf\u8d8a\u9ad8\uff0c\u6a21\u578b\u8d8a\u806a\u660e\u3002\u4f20\u7edf\u7a0b\u5e8f\u5199\u5b8c\u5c31\u5b9a\u578b\uff0cAI\u7a0b\u5e8f\u8d8a\u7528\u8d8a\u5f3a\u3002<\/li>\n<\/ol>\n
                        \n
                      1. \u4e24\u8005\u662f\u4e92\u8865\u5173\u7cfb\uff0c\u4e0d\u662f\u66ff\u4ee3\u5173\u7cfb<\/strong>\u2014\u2014\u786e\u5b9a\u6027\u4efb\u52a1\u7528\u4f20\u7edf\u7f16\u7a0b\uff0c\u6a21\u5f0f\u8bc6\u522b\u4efb\u52a1\u7528AI\u7f16\u7a0b\u3002\u5b9e\u9645\u9879\u76ee\u4e2d\u4e24\u8005\u7ecf\u5e38\u7ed3\u5408\u4f7f\u7528\u3002<\/li>\n<\/ol>\n
                        \n

                        \ud83d\udcdd \u4eca\u65e5\u884c\u52a8\u9879<\/h2>\n
                          \n
                        • [ ] \u8fd0\u884c\u4eca\u665a\u7684\u5783\u573e\u90ae\u4ef6\u5206\u7c7b\u4ee3\u7801\uff0c\u611f\u53d7\u4e24\u79cd\u7f16\u7a0b\u65b9\u5f0f\u7684\u533a\u522b<\/li>\n
                        • [ ] \u60f3\u4e00\u60f3\u4f60\u65e5\u5e38\u5de5\u4f5c\u4e2d\uff0c\u54ea\u4e9b\u4efb\u52a1\u9002\u5408\u7528AI\u7f16\u7a0b\uff08\u6a21\u5f0f\u8bc6\u522b\uff09\uff0c\u54ea\u4e9b\u9002\u5408\u7528\u4f20\u7edf\u7f16\u7a0b\uff08\u786e\u5b9a\u6027\u903b\u8f91\uff09<\/li>\n
                        • [ ] \u5c1d\u8bd5\u4fee\u6539\u8bad\u7ec3\u6570\u636e\uff08\u52a0\u51e0\u6761\u65b0\u7684\u5783\u573e\u90ae\u4ef6\u548c\u6b63\u5e38\u90ae\u4ef6\uff09\uff0c\u770b\u770b\u6a21\u578b\u51c6\u786e\u7387\u6709\u6ca1\u6709\u53d8\u5316<\/li>\n<\/ul>\n
                          \n

                          \ud83d\udcc5 \u4e0b\u7bc7\u9884\u544a<\/h2>\n

                          \u660e\u5929\u65e9\u8bfe Day07<\/strong>\uff1aAI\u4e94\u611f\u2014\u2014\u89c6\u89c9\u3001\u542c\u89c9\u3001\u8bed\u8a00\u3001\u63a8\u7406\u3001\u521b\u9020<\/p>\n

                          \n

                          AI\u80fd\u770b\u3001\u80fd\u542c\u3001\u80fd\u8bf4\u3001\u80fd\u60f3\u3001\u80fd\u521b\u9020\uff1f\u4e00\u6b21\u8bb2\u6e05AI\u7684\u4e94\u5927\u80fd\u529b\uff0c\u770b\u770b\u54ea\u4e9b\u5df2\u7ecf\u8d85\u8fc7\u4eba\u7c7b\u3002<\/p>\n<\/blockquote>\n

                          \u660e\u5929\u665a\u8bfe Day07<\/strong>\uff1a\u7b2c\u4e00\u5468\u590d\u76d8\u2014\u2014AI\u8ba4\u77e5\u5730\u56fe+\u81ea\u6d4b\u9898\uff08\u770b\u770b\u4f60\u5b66\u5230\u4e86\u591a\u5c11\uff09<\/p>\n

                          \n

                          7\u5929\u5b66\u4e60\u6210\u679c\u5927\u76d8\u70b9\uff0c\u7528\u4e00\u5f20\u8ba4\u77e5\u5730\u56fe\u4e32\u8054\u6240\u6709\u77e5\u8bc6\u70b9\uff0c\u8fd8\u6709\u81ea\u6d4b\u9898\u5e2e\u4f60\u67e5\u6f0f\u8865\u7f3a\u3002<\/p>\n<\/blockquote>\n

                          \n
                          \n

                          \ud83c\udf93 \u665a\u8bfe\u5408\u96c6<\/strong> | \u6bcf\u5929\u4e0b\u53485\u70b9\u66f4\u65b0<\/p>\n<\/blockquote>\n

                          \n

                          \ud83d\udca1 \u5b66\u5b8c\u8bb0\u5f97\u52a8\u624b\uff01\u8fd0\u884c\u4ee3\u7801\u6bd4\u770b\u4ee3\u7801\u6709\u7528100\u500d<\/p>\n<\/blockquote>\n

                          \n

                          \ud83d\udd16 \u672c\u6587\u5df2\u6536\u5f55\u81f3\u300cAI\u4e13\u5bb6\u517b\u6210\u8ba1\u5212\u00b7\u665a\u8bfe\u5b9e\u6218\u300d\u5408\u96c6\uff08album_id: 4522393809204510724\uff09<\/p>\n<\/blockquote>\n","protected":false},"excerpt":{"rendered":"

                          \ud83d\udcda AI\u4e13\u5bb6\u517b\u6210\u8ba1\u5212 \u00b7 \u7b2c11\u7bc7\uff08\u5171140\u7bc7\uff09 \u23f1\ufe0f \u9605\u8bfb\u65f6\u95f4\uff1a10-15\u5206\u949f \ud83c\udfaf \u9002\u5408\u4eba\u7fa4\uff1a\u96f6\u57fa\u7840\uff0c\u60f3\u4eb2\u624b […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[100],"tags":[],"class_list":["post-261","post","type-post","status-publish","format-standard","hentry","category-aixuexi"],"yoast_head":"\nDay06\u2014\u2014\u665a\u8bfe \u4f20\u7edf\u7f16\u7a0bvs AI\u7f16\u7a0b\uff1a\u601d\u7ef4\u65b9\u5f0f\u7684\u9769\u547d\u6027\u8f6c\u53d8 - \u6500\u5ca9\u8005<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/climbing.top\/index.php\/2026\/05\/22\/day06wanke-chuantongbianchengvs-aibianchengsiweifangshidegemingxingzhuanbian\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Day06\u2014\u2014\u665a\u8bfe \u4f20\u7edf\u7f16\u7a0bvs AI\u7f16\u7a0b\uff1a\u601d\u7ef4\u65b9\u5f0f\u7684\u9769\u547d\u6027\u8f6c\u53d8 - 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