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在信息爆炸的时代，自学已经成为许多人提升自我、拓展知识面的重要途径。自学不仅可以让我们在不受时间和地点限制的情况下学习，还能培养我们的自主学习能力和解决问题的能力。那么，如何自己打造一套完整的自学教程呢？以下是一份详细的指南。 ### 1. 明确学习目标 首先，你需要明确自己的学习目标。是想要提升某一领域的专业知识，还是为了解决生活中的实际问题？明确目标可以帮助你更有针对性地选择学习内容，避免盲目学习。 ### 2. 制定学习计划 在明确了学习目标之后，接下来就是制定学习计划。以下是一些建议： - **分解目标**：将大目标分解为若干个小目标，逐步实现。 - **设定时间表**：为每个小目标设定完成时间，确保学习进度。 - **分配学习资源**：根据学习内容，合理分配时间、精力，以及所需的学习资源。 ### 3. 选择合适的学习资源 自学过程中，选择合适的学习资源至关重要。以下是一些建议： - **网络资源**：利用互联网，你可以找到大量的学习资料，如在线课程、电子书、论坛等。 - **实体书籍**：针对特定领域，选择一本权威的实体书籍，有助于系统性地学习。 - **请教他人**：向专业人士请教，获取他们的经验和建议。 ### 4. 制定学习策略 为了提高学习效率，你需要制定适合自己的学习策略。以下是一些建议： - **主动学习**：积极参与课堂讨论、实践操作，提高学习兴趣。 - **总结归纳**：在学习过程中，及时总结归纳，形成自己的知识体系。 - **定期复习**：定期回顾所学内容，巩固记忆。 ### 5. 创造学习环境 一个良好的学习环境有助于提高学习效率。以下是一些建议： - **安静的环境**：选择一个安静、舒适的地方学习，避免外界干扰。 - **整理学习资料**：将学习资料分类整理，方便查找和使用。 - **合理规划时间**：合理安排学习时间，确保有足够的时间进行学习。 ### 6. 检验学习成果 学习过程中，检验学习成果非常重要。以下是一些建议： - **自我测试**：通过做题、做项目等方式，检验自己的学习成果。 - **与他人交流**：与同学、朋友或专业人士交流，了解自己的不足之处。 - **调整学习计划**：根据检验结果，调整学习计划，提高学习效果。 ### 7. 保持毅力 自学过程中，保持毅力至关重要。以下是一些建议： - **树立信心**：相信自己能够通过自学取得成功。 - **设定奖励机制**：为自己设定奖励，激励自己继续学习。 - **寻求支持**：向家人、朋友或专业人士寻求支持，共同进步。 总之，自学需要明确目标、制定计划、选择资源、制定策略、创造环境、检验成果和保持毅力。通过以上步骤，相信你能够打造一套适合自己的自学教程，实现自我提升。

TMTPOST --   Chinese telecommunications giant Huawei unveiled a sweeping expansion of its artificial intelligence computing ambitions on Thursday, announcing new supercomputing systems powered by its in-house Ascend chips. The move signals escalating competition with U.S. chipmaker Nvidia, the global leader in AI processors.AI-generated imageAt its annual Huawei Connect conference in Shanghai, the company introduced the forthcoming "Atlas 950 SuperCluster," which it said will begin rolling out as early as next year. The announcement underscores Huawei ’ s determination to challenge U.S. dominance in advanced AI hardware despite Washington ’ s tightening export controls on semiconductors."The competition has undeniably arrived and is gaining momentum," an Nvidia spokesperson said. "Customers will choose the best technology stack for running the world ’ s most popular commercial applications and open-source models."U.S. restrictions have cut China off from the most advanced chips needed to train large-scale AI models, forcing Chinese firms to rely on domestically developed processors or stockpiles of older foreign components. Huawei, long targeted by U.S. sanctions, has turned to large-scale clustering of its less powerful but increasingly refined Ascend processors. By combining vast numbers of these chips in interconnected networks, the company aims to match or surpass the performance of systems built on Nvidia ’ s hardware.Huawei said it plans to release three new versions of its Ascend chips through 2028, pledging to double computing power with each iteration. At the base level, its new Atlas 950 supernode will connect 8,192 Ascend chips. A full Atlas 950 SuperCluster would integrate more than 500,000 chips.A future upgrade, the Atlas 960 system slated for 2027, is projected to support nearly 15,500 chips per node and scale to more than 1 million Ascend processors across a full supercluster. Huawei claimed these would represent the world ’ s most powerful AI systems by raw computing power.Huawei did not shy away from comparisons with its American rival. Eric Xu, the company ’ s vice chairman and rotating chairman, said the Atlas 950 supernode would deliver 6.7 times more computing power than Nvidia ’ s upcoming NVL144 system. He went further, predicting Huawei would "be ahead on all fronts" compared with Nvidia ’ s 2027 offering — and claimed the Atlas 950 SuperCluster would even exceed the computing capacity of Elon Musk ’ s xAI Colossus project.While independent verification of Huawei ’ s claims is limited, industry experts said the company ’ s ambition is clear."Huawei ’ s announcement on its computing breakthrough is well timed with recent increasing emphasis by the Chinese government on self-reliance on China ’ s own chip technologies," said George Chen, partner and co-chair of the digital practice at The Asia Group. While noting that Huawei might be overstating its technical edge, he added, "the ambition to be a world AI leader cannot be underestimated."Research from SemiAnalysis earlier this year found that Huawei ’ s CloudMatrix system outperformed Nvidia ’ s equivalent despite each Ascend processor offering only about one-third the power of an Nvidia chip. The advantage came from scale: Huawei deployed roughly five times as many chips in its clusters."Computing power has and will continue to be the key for AI," Xu emphasized during his keynote speech in Shanghai.Huawei ’ s new announcements build on earlier efforts. Two years ago, it introduced the Atlas 900 SuperCluster, which it still markets with "thousands" of Ascend chips. On Thursday, the company said it had already deployed more than 300 Atlas 900 A3 supernodes to over 20 clients across telecoms, manufacturing, and other sectors.This momentum reflects a broader push within China to substitute foreign technology with homegrown alternatives. Beijing has increasingly pressed domestic firms to use local suppliers and invest in national semiconductor capabilities. Huawei ’ s breakthroughs are likely to be touted as proof of progress in these efforts.Huawei ’ s offensive comes as Nvidia faces growing headwinds in China, once one of its most important markets. Earlier this week, China extended an antitrust probe into the U.S. company over alleged monopolistic practices. On Wednesday, Nvidia ’ s shares fell more than 2% after the Financial Times reported that Beijing had ordered Chinese tech giants to halt testing and purchases of the RTX Pro 6000D, one of Nvidia ’ s new AI-focused chips.Nvidia Chief Executive Jensen Huang expressed frustration over the developments. "I am disappointed to hear this news," he told reporters, while reiterating that Huawei represents a "formidable" competitor.The geopolitical context adds another layer of complexity. The U.S. and China concluded trade talks in Spain this week that reportedly included discussions over the fate of TikTok, the video app owned by Beijing-based ByteDance. But technology co