Learning How to Learn(1)

学习一项技能,尤其是比较困难代学科,就像举重一样,不可能在比赛的前一天才开始训练。功夫在平时,每天锻炼,才能不断提高。在放松的时候大脑容易出现有创意的想法。
在睡觉时,大脑神经元根据学习内容会生长出新的突触,睡醒一觉,你已不同。不止学习,在其他方面也是使用的。学习技能,娱乐,运动,大脑在休息时会发展出新突触。
在学习过一段事件后大脑需要放松,把注意里转移到其他事情上,给大脑消化内容新建结构的时间,否则是填鸭式学习,大脑并没有理解知识点之间的关系。
记忆分工作记忆(短期记忆?)和长期记忆,工作记忆就像一块不太好用的黑板,即时书写却效果不佳。在工作时遇到困难经常需要通过工作记忆调动长期记忆,工作记忆和长期记忆之间的联系需要通过不断重复来巩固。但重复但频率,应该符合上面提到过的规律,即有间隔的重复,而不是把所有事情在一天之中完成。
睡眠,其实可以帮助清理头脑中“有毒物质“,人在清醒时,大脑是真的会产生些“有害物质“,他们的堆积会让人焦虑,暴躁等等。在睡觉时,脑细胞收缩,腾出空间让液体流过并带走这些物质。并且睡眠时,大脑会过滤掉一些相互联系混乱的突触,也就是会忘掉相互联系不太密切的事情,这个给更重要的事情腾出的记忆的空间。同时会加强强关系之间的联系,如果想要记忆的更加牢靠,可以在睡前复习一边相关内容。

As an AI language model, I can suggest the following steps to learn deep learning: 1. Learn the basics of machine learning: Before diving into deep learning, it is essential to understand the basics of machine learning, including supervised and unsupervised learning, regression, clustering, and classification. 2. Learn programming languages: To work with deep learning, one needs to have a strong foundation in programming languages such as Python and R, as most of the deep learning libraries are written in these languages. 3. Understand the mathematics behind deep learning: Deep learning involves a lot of math, including linear algebra, calculus, and probability. Understanding these concepts will help you better understand the algorithms used in deep learning. 4. Choose a deep learning framework: Popular deep learning frameworks include Tensorflow, Keras, PyTorch, and Caffe. Choose one and learn it. 5. Practice with datasets: Work with datasets to understand how deep learning works in practice. Kaggle is a great platform to get started with real-world datasets. 6. Read research papers: Read research papers to stay up-to-date with the latest advancements in deep learning. 7. Join communities: Join online communities such as Reddit, Discord, or GitHub to connect with other deep learning enthusiasts and learn from them. 8. Build projects: Building projects is the best way to learn deep learning. Start with simple projects and gradually move on to more complex ones. Remember, deep learning is a vast field, and it takes time and effort to master it. Keep practicing, and you will get there.
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