Advice on improving your programming skills

本文提供了提高编程能力的有效建议:多写代码、参与不同项目、精通一两门语言、阅读专业书籍和技术资料、加入讨论并撰写博客分享经验。

Advice on improving your programming skills

Source : sonic0002    Date : 2014-02-21 08:59:04  

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 pend 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 must 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 resolves the problems and your gains from the process. Others peoples 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

源码来自:https://pan.quark.cn/s/a3a3fbe70177 AppBrowser(Application属性查看器,不需要越狱! ! ! ) 不需要越狱,调用私有方法 --- 获取完整的已安装应用列表、打开和删除应用操作、应用运行时相关信息的查看。 支持iOS10.X 注意 目前AppBrowser不支持iOS11应用查看, 由于iOS11目前还处在Beta版, 系统API还没有稳定下来。 等到Private Header更新了iOS11版本,我也会进行更新。 功能 [x] 已安装的应用列表 [x] 应用的详情界面 (打开应用,删除应用,应用的相关信息展示) [x] 应用运行时信息展示(LSApplicationProxy) [ ] 定制喜欢的字段,展示在应用详情界面 介绍 所有已安装应用列表(应用icon+应用名) 为了提供思路,这里只用伪代码,具体的私有代码调用请查看: 获取应用实例: 获取应用名和应用的icon: 应用列表界面展示: 应用列表 应用运行时详情 打开应用: 卸载应用: 获取info.plist文件: 应用运行时详情界面展示: 应用运行时详情 右上角,从左往右第一个按钮用来打开应用;第二个按钮用来卸载这个应用 INFO按钮用来解析并显示出对应的LSApplicationProxy类 树形展示LSApplicationProxy类 通过算法,将LSApplicationProxy类,转换成了字典。 转换规则是:属性名为key,属性值为value,如果value是一个可解析的类(除了NSString,NSNumber...等等)或者是个数组或字典,则继续递归解析。 并且会找到superClass的属性并解析,superClass如...
基于遗传算法辅助异构改进的动态多群粒子群优化算法(GA-HIDMSPSO)的LSTM分类预测研究(Matlab代码实现)内容概要:本文研究了一种基于遗传算法辅助异构改进的动态多群粒子群优化算法(GA-HIDMSPSO),并将其应用于LSTM神经网络的分类预测中,通过Matlab代码实现。该方法结合遗传算法的全局搜索能力与改进的多群粒子群算法的局部优化特性,提升LSTM模型在分类任务中的性能表现,尤其适用于复杂非线性系统的预测问题。文中详细阐述了算法的设计思路、优化机制及在LSTM参数优化中的具体应用,并提供了可复现的Matlab代码,属于SCI级别研究成果的复现与拓展。; 适合人群:具备一定机器学习和优化算法基础,熟悉Matlab编程,从事智能算法、时间序列预测或分类模型研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①提升LSTM在分类任务中的准确性与收敛速度;②研究混合智能优化算法(如GA与PSO结合)在神经网络超参数优化中的应用;③实现高精度分类预测模型,适用于电力系统故障诊断、电池健康状态识别等领域; 阅读建议:建议读者结合Matlab代码逐步调试运行,理解GA-HIDMSPSO算法的实现细节,重点关注种群划分、异构策略设计及与LSTM的集成方式,同时可扩展至其他深度学习模型的参数优化任务中进行对比实验。
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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