Advice on improving your programming skills

本文提供了几个实用建议帮助初学者及进阶者快速提高编程水平,包括多写代码、参与实际项目、精通一到两种编程语言、阅读专业书籍、参与讨论、阅读内部技术资料、撰写博客等。

Programming is cool. But behind the scenes it's also difficult for many people. Many people are defeated at the early stage of learning programming. When you are not so familiar with programming, you may find you don't know where to start and what to start with first and where to apply the knowledge. Once you go though the tough period of the learning phase, you will find a whole new world. Below are some advice which can help you improve your programming skills quickly.

Write more code.  The best thing to learn something quickly is practicing. You should spend more of your time on building and writing code since you won't get better unless you practice the craft.  You may start by writing some simple programs with the basic knowledge you have about the language.

Work on different types of projects. After you gain enough familiarity of one language, you should start to work on something real. This will help you have a comprehensive understanding of the capability of the language This will in turn push you to learn other stuff related to the language. Later ff you find yourself always doing similar tasks using similar methods, it's going to be hard to get out of your comfort zone and to pick up new skills.

Master one or two programming languages that you use.  Read a good book or two on the languages.  Focus on developing a solid grasp of the advanced concepts in that language, and gain familiarity with core, language libraries.  Make sure that at least one of your languages is a scripting language.

Start reading.  Read as many books as possible. Books usually can give you a systematic graph about a language and they will lead you through all the aspects of a language This is especially useful for beginners  Here's a start: What is the single most influential book every programmer should read?

Join discussions. When discussing with other people, you will find new ideas or thoughts you may not notice before. During this phase, you can also learn from other people and it's also a good chance for you to test how well you grasp a language by sharing your opinions about programming.

Read through any technical, educational material available internally. Google, for instance, has a wide array of codelabs that teach core abstractions and high-quality guides of best practices that veteran engineers have written for various languages based on decades of experience.  If your company doesn't have similar resources, Google's open sourced some of their guides: https://code.google.com/p/google....

Write blogs. While learning programming, you may encounter different issues and you will try to defeat the problems with all means. It's a good chance for you to write down the process you resolve the problems and your gains from the process. Others can benefit from your experience as well. You can also make friends with people who have similar interests with you.

As for the time spent on learning programming. You should start by carving out 20% of your time to devote to your own skills development.  If possible, it'll be better if that 20% comes from one or two hours a day rather than a day a week because you can then make a daily habit out of improving your skills.  Your productivity may decrease initially (or it might not change much if you're replacing web surfing or other distractions), but the goal is to make investments that will make you more effective in the long run.

带开环升压转换器和逆变器的太阳能光伏系统 太阳能光伏系统驱动开环升压转换器和SPWM逆变器提供波形稳定、设计简单的交流电的模型 Simulink模型展示了一个完整的基于太阳能光伏的直流到交流电力转换系统,该系统由简单、透明、易于理解的模块构建而成。该系统从配置为提供真实直流输出电压的光伏阵列开始,然后由开环DC-DC升压转换器进行处理。升压转换器将光伏电压提高到适合为单相全桥逆变器供电的稳定直流链路电平。 逆变器使用正弦PWM(SPWM)开关来产生干净的交流输出波形,使该模型成为研究直流-交流转换基本操作的理想选择。该设计避免了闭环和MPPT的复杂性,使用户能够专注于光伏接口、升压转换和逆变器开关的核心概念。 此模型包含的主要功能: •太阳能光伏阵列在标准条件下产生~200V电压 •具有固定占空比操作的开环升压转换器 •直流链路电容器,用于平滑和稳定转换器输出 •单相全桥SPWM逆变器 •交流负载,用于观察实际输出行为 •显示光伏电压、升压输出、直流链路电压、逆变器交流波形和负载电流的组织良好的范围 •完全可编辑的结构,适合分析、实验和扩展 该模型旨在为太阳能直流-交流转换提供一个干净高效的仿真框架。布局简单明了,允许用户快速了解信号流,检查各个阶段,并根据需要修改参数。 系统架构有意保持模块化,因此可以轻松扩展,例如通过添加MPPT、动态负载行为、闭环升压控制或并网逆变器概念。该模型为进一步开发或整合到更大的可再生能源模拟中奠定了坚实的基础。
知识蒸馏(Knowledge Distillation, KD)作为模型压缩的一种有效手段,近年来在深度学习领域得到了广泛研究。通过将复杂模型(教师模型)的知识迁移到较小的模型(学生模型)中,可以显著减少计算资源消耗,同时保持较高的性能[^2]。然而,传统的知识蒸馏方法在处理高维特征空间时可能会遇到信息冗余或噪声干扰的问题,这限制了学生模型的学习效果。 为了进一步提升知识蒸馏的效果,研究人员开始探索基于正交投影(Orthogonal Projections)的技术。正交投影的核心思想是将教师模型和学生模型的特征空间映射到一个共享的正交子空间中,从而减少冗余信息并增强关键特征的表达能力。这一方法在多个方面提升了知识蒸馏的表现: ### 正交投影在知识蒸馏中的应用 1. **特征对齐与降维** 正交投影可以通过构建一个低维的正交基来对教师模型和学生模型的特征进行对齐。这样不仅可以降低特征维度,还能保留最重要的语义信息。具体来说,使用主成分分析(PCA)或线性判别分析(LDA)等方法,可以提取出最具判别性的特征方向,并将学生模型的输出投影到这些方向上,从而实现更有效的知识迁移。 2. **损失函数设计** 在传统知识蒸馏中,通常使用KL散度或均方误差(MSE)作为损失函数来衡量教师模型和学生模型之间的差异。引入正交投影后,可以在投影后的特征空间中定义新的损失函数,例如使用余弦相似度或正交损失(Orthogonal Loss)来鼓励学生模型学习与教师模型一致的方向。例如,以下是一个基于余弦相似度的损失函数示例: ```python import torch import torch.nn as nn class OrthogonalProjectionLoss(nn.Module): def __init__(self): super(OrthogonalProjectionLoss, self).__init__() self.cos_sim = nn.CosineSimilarity(dim=-1) def forward(self, teacher_features, student_features): # Normalize features teacher_features = F.normalize(teacher_features, p=2, dim=1) student_features = F.normalize(student_features, p=2, dim=1) # Compute cosine similarity similarity = self.cos_sim(teacher_features, student_features) loss = 1 - similarity.mean() return loss ``` 3. **多视角学习与正交性约束** 在某些任务中,如图像到视频的重识别(Image-to-Video Re-ID),学生模型可以从多个视角中学习教师模型的知识。通过引入正交性约束,可以确保学生模型在不同视角下的特征表示具有良好的区分能力。例如,在Views Knowledge Distillation (VKD) 中,学生模型被要求在较少的视角下恢复教师模型在多个视角下的特征表示,从而提升其泛化能力和鲁棒性[^3]。 4. **信号传播分析与正交性优化** 对于大型语言模型(LLM),信号传播分析(Signal Propagation Analysis)可以用于理解模型内部的信息流动。结合正交投影技术,可以优化学生模型的信号传播路径,使其更接近教师模型的行为。具体而言,通过对教师模型和学生模型的中间层特征进行正交分解,可以识别出对最终输出影响最大的特征方向,并在训练过程中对这些方向进行重点优化[^1]。 ### 结论 基于正交投影的知识蒸馏方法在多个方面提升了传统KD的效果。通过特征对齐、损失函数设计、多视角学习以及信号传播分析,学生模型能够更有效地从教师模型中提取关键知识,从而在保持较小模型规模的同时实现更高的性能。未来的研究方向可能包括更高效的正交投影算法设计、动态调整投影空间的方法,以及在不同任务和模型架构中的广泛应用。
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