🧠:`nofollow` 是一个 HTML 标签属性，最初由 Google 在 2005 年提出，目的是为了减少垃圾链接对搜索引擎排名的影响。当一个网页中的链接被加上 `nofollow` 属性时，搜索引擎的爬虫在抓取这个链接时，不会将其计入页面的权重计算，也不会通过这个链接传递权重给目标页面。这意味着，即使一个页面有很多指向其他页面的 `nofollow` 链接，这些链接也不会帮助目标页面在搜索引擎的排名中提升。`nofollow` 属性通过在 `<a>` 标签中添加 `rel="nofollow"` 来实现。例如：```html<a href="http://example.com" rel="nofollow">Example Link</a>```除了 Google，其他搜索引擎如 Yahoo 和 Bing 也支持 `nofollow` 属性。随着时间的推移，`nofollow` 的功能也有所扩展，例如，Google 在 2019 年引入了 `noreferrer` 和 `ugc` 属性，以提供更细粒度的控制。`noreferrer` 用于控制引用信息的传递，而 `ugc`（User Generated Content）用于标记用户生成的内容，如论坛帖子和评论，以帮助搜索引擎更好地理解链接的上下文。使用 `nofollow` 的常见场景包括：1. 防止垃圾链接：在用户可以提交内容的区域，如评论和论坛，使用 `nofollow` 可以防止垃圾链接影响网站的排名。2. 控制权重传递：在某些情况下，网站管理员可能不希望某些链接传递权重，例如在广告或赞助内容中。3. 提高网站内部链接结构的效率：通过在不重要的链接上使用 `nofollow`，可以确保搜索引擎将更多的权重分配给更重要的页面。总之，`nofollow` 是一个重要的 SEO 工具，可以帮助网站管理员控制搜索引擎如何理解和处理网站上的链接。

🧠:费马大定理，也称为费马最后定理，是数论中的一个著名问题，由17世纪法国数学家皮埃尔·德·费马提出。该定理的陈述是：对于任何大于2的整数n，方程a^n + b^n = c^n没有正整数解a、b和c。费马大定理在数学界引起了极大的兴趣，但直到350多年后，才由英国数学家安德鲁·怀尔斯在1994年证明。怀尔斯的证明使用了现代数学的许多高级工具，包括椭圆曲线、模形式和伽罗瓦表示理论。怀尔斯的证明被认为是20世纪最伟大的数学成就之一，它不仅解决了费马大定理，还推动了数论和代数几何领域的发展。

🧠:在Java中，线程间通信通常使用synchronized关键字和wait(), notify()方法。以下是一个简单的生产者消费者问题的示例，展示了两个线程（生产者和消费者）如何进行交互。```javapublic class ThreadCommunicationExample { private final Object lock = new Object(); private String data; private boolean ready = false; private class Producer extends Thread { @Override public void run() { synchronized (lock) { System.out.println("Producer thread is waiting..."); try { lock.wait(); } catch (InterruptedException e) { e.printStackTrace(); } data = "Some data"; ready = true; System.out.println("Producer thread is producing data..."); lock.notify(); } } } private class Consumer extends Thread { @Override public void run() { synchronized (lock) { System.out.println("Consumer thread is waiting..."); while (!ready) { try { lock.wait(); } catch (InterruptedException e) { e.printStackTrace(); } } System.out.println("Consumer thread is consuming data: " + data); ready = false; lock.notify(); } } } public void start() { new Producer().start(); new Consumer().start(); } public static void main(String[] args) { new ThreadCommunicationExample().start(); }}```在这个例子中，生产者线程和消费者线程都使用同一个锁对象。生产者线程在产生数据之前等待，而消费者线程在数据未准备好时等待。当生产者线程产生数据后，它会调用notify()方法唤醒消费者线程，然后消费者线程可以消费数据。当消费者线程消费完数据后，它会再次调用notify()方法唤醒生产者线程，使其可以产生新的数据。

🧠:要实现这个需求，可以使用以下SQL语句：```sqlSELECT tid, tnameFROM teachersWHERE score >= 4.0ORDER BY score DESC;```这条SQL语句的解释如下：- `SELECT tid, tname`：选择`tid`（教师编号）和`tname`（教师姓名）这两个字段。- `FROM teachers`：指定从`teachers`表中查询数据。- `WHERE score >= 4.0`：添加一个条件，只选择那些评分大于或等于4.0的记录。- `ORDER BY score DESC`：按照`score`字段的值进行降序排序，确保评分高的教师排在前面。这样，执行这条SQL语句后，你将得到所有评分大于或等于4.0的教师的编号和姓名，且结果按照评分从高到低排序。

🧠:TensorFlow的运算（Ops）数量非常多，涵盖了各种数学运算、数据处理、神经网络层、控制流等等。以下是一些常见的Ops，但并非全部：1. 数学运算：Add, Subtract, Multiply, Divide, MatMul, Square, Sqrt, Exp, Log, Pow, Abs, Neg, Sign, Minimum, Maximum, Floor, Ceil, Round, Mod, Complex, Real, Imag, Conjugate, AddN, AccumulateNV2, AddV2, BiasAdd, BiasAddGrad, Cross, DivNoNan, FloorDiv, FloorMod, Igammac, IgammacGrad, Igamma, IgammaGrad, Lbeta, LbetaGrad, MulNoNan, Polygamma, RealDiv, Rint, Rsqrt, Sigmoid, Sin, Sinh, SquareDifference, Tan, Tanh, Xdivy, Xlogy, Xlog1py, Zeta2. 数据处理：BatchToSpace, BatchToSpaceND, Concat, ConcatV2, Const, DepthToSpace, DepthwiseConv2dNative, ExpandDims, Fill, Gather, GatherNd, GatherV2, Identity, InvertPermutation, MatrixDiag, MatrixDiagPart, MatrixSetDiag, OneHot, Pad, Pack, PadV2, ParallelConcat, Range, Rank, Reshape, ResizeArea, ResizeBicubic, ResizeBilinear, ResizeNearestNeighbor, Reverse, ReverseSequence, Roll, ScatterNd, ScatterNdAdd, ScatterNdSub, ScatterNdUpdate, Shape, Size, Slice, SpaceToBatch, SpaceToBatchND, SpaceToDepth, Split, SplitV, Squeeze, StridedSlice, Tile, Transpose, Unique, UniqueWithCounts, Unpack, Where, ZerosLike3. 神经网络层：AvgPool, AvgPool3D, AvgPool3DGrad, AvgPoolGrad, Conv2D, Conv2DBackpropFilter, Conv2DBackpropInput, Conv3D, Conv3DBackpropFilterV2, Conv3DBackpropInputV2, Conv3DGrad, DepthwiseConv2dNative, DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropInput, Dropout, DropoutGenMask, DropoutGrad, DropoutV2, Elu, EluGrad, FusedBatchNorm, FusedBatchNormV2, FusedBatchNormV3, FusedResizeAndPadConv2D, InTopK, L2Loss, LRN, LRNGrad, LogSoftmax, MaxPool, MaxPool3D, MaxPool3DGrad, MaxPoolGrad, MaxPoolGradWithArgmax, MaxPoolV2, MaxPoolWithArgmax, Mean, MeanV2, Moments, NthElement, NthLexicographicalElement, QuantizedAvgPool, QuantizedMaxPool, QuantizedReluX, QuantizedReluXGrad, Relu, Relu6, Relu6Grad, ReluGrad, Rnn, RnnBlockCell, RnnBlockFusedCell, RnnBlockFusedCellGrad, RnnBlockGRU, RnnBlockLSTM, RnnCell, RnnCellV2, RnnCellWithState, RnnCellWithStateV2, RnnCellWithStateV3, RnnCellWithStateV4, RnnCellWithStateV5, RnnCellWithStateV6, RnnCellWithStateV7, RnnCellWithStateV8, RnnCellWithStateV9, RnnCellWithStateV10, RnnCellWithStateV11, RnnCellWithStateV12, RnnCellWithStateV13, RnnCellWithStateV14, RnnCellWithStateV15, RnnCellWithStateV16, RnnCellWithStateV17, RnnCellWithStateV18, RnnCellWithStateV19, RnnCellWithStateV20, RnnCellWithStateV21, RnnCellWithStateV22, RnnCellWithStateV23, RnnCellWithStateV24, RnnCellWithStateV25, RnnCellWithStateV26, RnnCellWithStateV27, RnnCellWithStateV28, RnnCellWithStateV29, RnnCellWithStateV30, RnnCellWithStateV31, RnnCellWithStateV32, RnnCellWithStateV33, RnnCellWithStateV34, RnnCellWithStateV35, RnnCellWithStateV36, RnnCellWithStateV37, RnnCellWithStateV38, RnnCellWithStateV39, RnnCellWithStateV40, RnnCellWithStateV41, RnnCellWithStateV42, RnnCellWithStateV43, RnnCellWithStateV44, RnnCellWithStateV45, RnnCellWithStateV46, RnnCellWithStateV47, RnnCellWithStateV48, RnnCellWithStateV49, RnnCellWithStateV50, RnnCellWithStateV51, RnnCellWithStateV52, RnnCellWithStateV53, RnnCellWithStateV54, RnnCellWithStateV55, RnnCellWithStateV56, RnnCellWithStateV57, RnnCellWithStateV58, RnnCellWithStateV59, RnnCellWithStateV60, RnnCellWithStateV61, RnnCellWithStateV62, RnnCellWithStateV63, RnnCellWithStateV64, RnnCellWithStateV65, RnnCellWithStateV66, RnnCellWithStateV67, RnnCellWithStateV68, RnnCellWithStateV69, RnnCellWithStateV70, RnnCellWithStateV71, RnnCellWithStateV72, RnnCellWithStateV73, RnnCellWithStateV74, RnnCellWithStateV75, RnnCellWithStateV76, RnnCellWithStateV77, RnnCellWithStateV78, RnnCellWithStateV79, RnnCellWithStateV80, RnnCellWithStateV81, RnnCellWithStateV82, RnnCellWithStateV83, RnnCellWithStateV84, RnnCellWithStateV85, RnnCellWithStateV86, RnnCellWithStateV87, RnnCellWithStateV88, RnnCellWithStateV89, RnnCellWithStateV90, RnnCellWithStateV91, RnnCellWithStateV92, RnnCellWithStateV93, RnnCellWithStateV94, RnnCellWithStateV95, RnnCellWithStateV96, RnnCellWithStateV97, RnnCellWithStateV98, RnnCellWithStateV99, RnnCellWithStateV100, RnnCellWithStateV101, RnnCellWithStateV102, RnnCellWithStateV103, RnnCellWithStateV104, RnnCellWithStateV105, RnnCellWithStateV106, RnnCellWithStateV107, RnnCellWithStateV108, RnnCellWithStateV109, RnnCellWithStateV110, RnnCellWithStateV111, RnnCellWithStateV112, RnnCellWithStateV113, RnnCellWithStateV114, RnnCellWithStateV115, RnnCellWithStateV116, RnnCellWithStateV117, RnnCellWithStateV118, RnnCellWithStateV119, RnnCellWithStateV120, RnnCellWithStateV121, RnnCellWithStateV122, RnnCellWithStateV123, RnnCellWithStateV124, RnnCellWithStateV125, RnnCellWithStateV126, RnnCellWithStateV127, RnnCellWithStateV128, RnnCellWithStateV129, RnnCellWithStateV130, RnnCellWithStateV131, RnnCellWithStateV132, RnnCellWithStateV133, RnnCellWithStateV134, RnnCellWithStateV135, RnnCellWithStateV136, RnnCellWithStateV137, RnnCellWithStateV138, RnnCellWithStateV139, RnnCellWithStateV140, RnnCellWithStateV141, RnnCellWithStateV142, RnnCellWithStateV143, RnnCellWithStateV144, RnnCellWithStateV145, RnnCellWithStateV146, RnnCellWithStateV147, RnnCellWithStateV148, RnnCellWithStateV149, RnnCellWithStateV150, RnnCellWithStateV151, RnnCellWithStateV152, RnnCellWithStateV153, RnnCellWithStateV154, RnnCellWithStateV155, RnnCellWithStateV156, RnnCellWithStateV157, RnnCellWithStateV158, RnnCellWithStateV159, RnnCellWithStateV160, RnnCellWithStateV161, RnnCellWithStateV162, RnnCellWithStateV163, RnnCellWithStateV164, RnnCellWithStateV165, RnnCellWithStateV166, RnnCellWithStateV167, RnnCellWithStateV168, RnnCellWithStateV169, RnnCellWithStateV170, RnnCellWithStateV171, RnnCellWithStateV172, RnnCellWithStateV173, RnnCellWithStateV174, RnnCellWithStateV175, RnnCellWithStateV176, RnnCellWithStateV177, RnnCellWithStateV178, RnnCellWithStateV179, RnnCellWithStateV180, RnnCellWithStateV181, RnnCellWithStateV182, RnnCellWithStateV183, RnnCellWithStateV184, RnnCellWithStateV185, RnnCellWithStateV186, RnnCellWithStateV187, RnnCellWithStateV188, RnnCellWithStateV189, RnnCellWithStateV190, RnnCellWithStateV191, RnnCellWithStateV192, RnnCellWithStateV193, RnnCellWithStateV194, RnnCellWithStateV195, RnnCellWithStateV196, RnnCellWithStateV197, RnnCellWithStateV198, RnnCellWithStateV199, RnnCellWithStateV200, RnnCellWithStateV201, RnnCellWithStateV202, RnnCellWithStateV203, RnnCellWithStateV204, RnnCellWithStateV205, RnnCellWithStateV206, RnnCellWithStateV207, RnnCellWithStateV208, RnnCellWithStateV209, RnnCellWithStateV210, RnnCellWithStateV211, RnnCellWithStateV212, RnnCellWithStateV213, RnnCellWithStateV214, RnnCellWithStateV215, RnnCellWithStateV216, RnnCellWithStateV217, RnnCellWithStateV218, RnnCellWithStateV219, RnnCellWithStateV220, RnnCellWithStateV221, RnnCellWithStateV222, RnnCellWithStateV223, RnnCellWithStateV224, RnnCellWithStateV225, RnnCellWithStateV226, RnnCellWithStateV227, RnnCellWithStateV228, RnnCellWithStateV229, RnnCellWithStateV230, RnnCellWithStateV231, RnnCellWithStateV232, RnnCellWithStateV233, RnnCellWithStateV234, RnnCellWithStateV235, RnnCellWithStateV236, RnnCellWithStateV237, RnnCellWithStateV238, RnnCellWithStateV239, RnnCellWithStateV240, RnnCellWithStateV241, RnnCellWithStateV242, RnnCellWithStateV243, RnnCellWithStateV244, RnnCellWithStateV245, RnnCellWithStateV246, RnnCellWithStateV247, RnnCellWithStateV248, RnnCellWithStateV249, RnnCellWithStateV250, RnnCellWithStateV251, RnnCellWithStateV252, RnnCellWithStateV253, RnnCellWithStateV254, RnnCellWithStateV255, RnnCellWithStateV256, RnnCellWithStateV257, RnnCellWithStateV258, RnnCellWithStateV259, RnnCellWithStateV260, RnnCellWithStateV261, RnnCellWithStateV262, RnnCellWithStateV263, RnnCellWithStateV264, RnnCellWithStateV265, RnnCellWithStateV266, RnnCellWithStateV267, RnnCellWithStateV268, RnnCellWithStateV269, RnnCellWithStateV270, RnnCellWithStateV271, RnnCellWithStateV272, RnnCellWithStateV273, RnnCellWithStateV274, RnnCellWithStateV275, RnnCellWithStateV276, RnnCellWithStateV277, RnnCellWithStateV278, RnnCellWithStateV279, RnnCellWithStateV280, RnnCellWithStateV281, RnnCellWithStateV282, RnnCellWithStateV283, RnnCellWithStateV284, RnnCellWithStateV285, RnnCellWithStateV286, RnnCellWithStateV287, RnnCellWithStateV288, RnnCellWithStateV289, RnnCellWithStateV290, RnnCellWithStateV291, RnnCellWithStateV292, RnnCellWithStateV293, RnnCellWithStateV294, RnnCellWithStateV295, RnnCellWithStateV296, RnnCellWithStateV297, RnnCellWithStateV298, RnnCellWithStateV299, RnnCellWithStateV300, RnnCellWithStateV301, RnnCellWithStateV302, RnnCellWithStateV303, RnnCellWithStateV304, RnnCellWithStateV305, RnnCellWithStateV306, RnnCellWithStateV307, RnnCellWithStateV308, RnnCellWithStateV309, RnnCellWithStateV310, RnnCellWithStateV311, RnnCellWithStateV312, RnnCellWithStateV313, RnnCellWithStateV314, RnnCellWithStateV315, RnnCellWithStateV316, RnnCellWithStateV317, RnnCellWithStateV318, RnnCellWithStateV319, RnnCellWithStateV320, RnnCellWithStateV321, RnnCellWithStateV322, RnnCellWithStateV323, RnnCellWithStateV324, RnnCellWithStateV325, RnnCellWithStateV326, RnnCellWithStateV327, RnnCellWithStateV328, RnnCellWithStateV329, RnnCellWithStateV330, RnnCellWithStateV331, RnnCellWithStateV332, RnnCellWithStateV333, RnnCellWithStateV334, RnnCellWithStateV335, RnnCellWithStateV336, RnnCellWithStateV337, RnnCellWithStateV338, RnnCellWithStateV339, RnnCellWithStateV340, RnnCellWithStateV341, RnnCellWithStateV342, RnnCellWithStateV343, RnnCellWithStateV344, RnnCellWithStateV345, RnnCellWithStateV346, RnnCellWithStateV347, RnnCellWithStateV348, RnnCellWithStateV349, RnnCell

🧠:当然，选择阅读的书籍应该基于你的兴趣、目标和你希望在新的一年里探索的主题。以下是一些跨不同类别的书籍建议，它们在2023年可能会引起你的兴趣：1. 小说类： - 《无声告白》 by 伍绮诗 - 一部探讨身份、家庭和秘密的深刻小说。 - 《百年孤独》 by 加西亚·马尔克斯 - 一部魔幻现实主义的经典之作，讲述了布恩迪亚家族七代人的故事。2. 非小说类： - 《人类简史》 by 尤瓦尔·赫拉利 - 从认知革命到21世纪，对人类历史的全面回顾。 - 《原则》 by 雷·达里奥 - 一位成功投资者分享的关于生活和工作的原则。3. 科幻与奇幻： - 《三体》 by 刘慈欣 - 中国科幻文学的里程碑作品，探索了人类与外星文明的首次接触。 - 《哈利·波特与魔法石》 by J.K.罗琳 - 如果你还没有读过，这是一个开始的好地方，进入一个充满魔法的世界。4. 心理学与个人成长： - 《思考，快与慢》 by 丹尼尔·卡内曼 - 探讨了我们的思维模式如何影响决策。 - 《原子习惯》 by 詹姆斯·克利尔 - 提供了建立和维持良好习惯的实用策略。5. 历史与文化： - 《枪炮、病菌与钢铁》 by 贾雷德·戴蒙德 - 分析了为什么一些社会在历史中取得了优势。 - 《丝绸之路》 by 彼得·弗兰科潘 - 从古代到现代，丝绸之路对世界历史的影响。6. 经济与商业： - 《穷查理宝典》 by 查理·芒格 - 伯克希尔·哈撒韦公司副董事长的智慧集锦。 - 《创新者的窘境》 by 克莱顿·克里斯坦森 - 探讨了为什么好公司会失败，以及如何避免这种命运。选择书籍时，考虑你的兴趣和目标，这样你就可以从阅读中获得最大的乐趣和价值。希望这些建议能帮助你开始一个充满知识和启发的阅读之旅！