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Chapter I: Fundamentals 第一章 基本原理

This section focuses on: What is AI. 本节重点介绍：什么是 AI。

1. Definition 定义

  • AI (Artificial Intelligence) aims to create machines that can perform tasks requiring human-like intelligence. AI（人工智能）旨在创造能够执行需要类似人类智能的任务的机器。
  • ML (Machine learning) is like teaching computers to spot patterns and make decisions based on data without being explicitly programmed for those tasks. ML（机器学习）就像教计算机发现模式并根据数据做出决策，而无需为这些任务进行明确编程。

AI: Includes learning, decision-making, and problem-solving. 人工智能：包括学习、决策和解决问题。
ML: Giving computers the ability to learn from examples. Just like how you might learn to recognize a cat by looking at lots.机器学习：让计算机能够从示例中学习。就像你如何通过看地段来学习识别一只猫一样。

2. Background skills 背景技能

Don’t worry, you don’t have to be an expert in these. 别担心，您不必成为这些方面的专家。

Math 数学

  • Calculus: Concepts like derivatives and integrals are foundational. 微积分：导数和积分等概念是基础。
  • Linear Algebra: Know your way around matrices, vectors, and eigenvalues. 线性代数：了解矩阵、向量和特征值。
  • Probability and Statistics: Learn distributions, mean, median, etc. 概率论和数理统计：学习分布、平均值、中位数等。

Programming 编程

  • Python: Master Python syntax and commonly used libraries. Python：掌握Python语法和常用库。
  • TensorFlow/PyTorch: Pick at least one machine learning framework to learn. TensorFlow/PyTorch：至少选择一个机器学习框架进行学习。

Algorithmic 算法

  • Understand basic algorithms and data structures: arrays, linked lists, trees, and graphs. 了解基本算法和数据结构：数组、链表、树和图形。

Don’t be scared, though — you don’t need to know deep math to use AI. You’ll only need it if you plan on going super deep into building AI tools and whatnot. 不过，不要害怕——你不需要知道深奥的数学知识就可以使用人工智能。只有当您计划深入构建 AI 工具等时，您才需要它。

3. How to study AI 如何学习人工智能

Self-Study 自学

  • Utilize online courses, tutorials, and research papers. Coursera, Udacity, and edX are platforms to consider. 利用在线课程、教程和研究论文。Coursera、Udacity 和 edX 是可以考虑的平台。

Bootcamps 训练营

  • These are intensive, short-term programs focused on practical skills.
    这些是专注于实践技能的密集短期课程。

Formal Education 正规教育

  • Degrees in Computer Science_, _Data Science or AI provide a structured environment and networking opportunities. Obviously, this will take too long.
    计算机科学、数据科学或人工智能学位提供了一个结构化的环境和交流机会。 显然，这将花费太长时间。

That’s why you’re better of with courses. 这就是为什么你更喜欢课程的原因。

I’ve got a list for the 10 best AI courses. 我列出了 10 门最佳 AI 课程。

4. How AI works 人工智能的工作原理

These details sit at the base of Artificial Intelligence. 这些细节是人工智能的基础。

Machine Learning Types 机器学习类型

  • Supervised Learning: Works with labeled data. Examples include regression and classification. 监督学习：使用标记数据。示例包括回归和分类。
  • Unsupervised Learning: Focuses on unlabeled data. Examples include clustering and association. 无监督学习：专注于未标记的数据。示例包括聚类分析和关联。
  • Reinforcement Learning: The model learns by interacting with an environment to achieve a goal or objective. 强化学习：模型通过与环境交互来实现目标或目的进行学习。

Neural Networks 神经网络

  • Understand the architecture: input layer, hidden layers, and output layer. 了解架构：输入层、隐藏层和输出层。

NLP: Natural Language Processing 自然语言处理

  • Techniques to understand and generate human language. Covers text analysis, language models, and translation. 理解和生成人类语言的技术。涵盖文本分析、语言模型和翻译。

Computer Vision 计算机视觉

  • Algorithms for image and video recognition, including object detection and segmentation. 用于图像和视频识别的算法，包括对象检测和分割。

5. Practical skills 实践技能

Data Cleaning and Preprocessing 数据清洗和预处理

  • Techniques for handling missing data, normalization, and encoding. 用于处理缺失数据、规范化和编码的技术。

Model Training and Evaluation 模型训练和评估

  • Understand metrics like accuracy, precision, recall, and F1 score. 了解准确率、精确率、召回率和 F1 分数等指标。

Deployment 部署

  • Learn how to deploy your model into a production environment, using technologies like Docker and cloud services. 了解如何使用 Docker 和云服务等技术将模型部署到生产环境中。

6. Build a portfolio 建立投资组合

GitHub 代码仓库

  • Keep your code and projects on GitHub to showcase your skills. 将您的代码和项目保留在 GitHub 上以展示您的技能。

Competitions 比赛

  • Participate in Kaggle competitions to test and improve your skills. 参加 Kaggle 比赛以测试和提高您的技能。

Open Source Contributions 开源贡献

  • Contribute to open-source projects related to AI. 为与 AI 相关的开源项目做出贡献。

7. Networking 网络

LinkedIn

  • Use LinkedIn to connect with industry professionals and to look for job opportunities. 使用 LinkedIn 与行业专业人士联系并寻找工作机会。

Conferences and Webinars 会议和网络研讨会

  • Attend to stay updated and to network with others in the field. 参加以保持最新状态并与该领域的其他人建立联系。

Communities 社区

  • Reddit, Indie Hackers, and various Twitter communities may be of help. Reddit、Indie Hackers 和各种 Twitter 社区可能会有所帮助。

Furthermore, even Hacker News is a great place. 此外，即使是 Hacker News 也是一个很棒的地方。

8. Job opportunities 工作机会

Roles 角色

  • Machine Learning Engineer, Data Scientist, AI Research Scientist, etc. 机器学习工程师、数据科学家、人工智能研究科学家等

Industries 行业

  • AI has applications in healthcare, finance, automotive, retail, and more. 人工智能在医疗保健、金融、汽车、零售等领域都有应用。

Finance 金融

  • Algorithmic trading, fraud detection, and customer service, leading to job openings for data analysts, machine learning specialists, and financial advisors. 算法交易、欺诈检测和客户服务，为数据分析师、机器学习专家和财务顾问提供职位空缺。

Content making 内容制作

  • AI-driven tools can generate articles, blog posts, and social media content, reducing the need for manual content creation. AI 驱动的工具可以生成文章、博客文章和社交媒体内容，从而减少手动创建内容的需求

Start making AI content with the most powerful AI Generation Tools. 开始使用最强大的 AI 生成工具制作 AI 内容。

Chapter II: How to use AI 第二章 如何使用人工智能

In this section, I’ll speak about understanding the basics when actually starting to use AI. 在本节中，我将讨论在实际开始使用 AI 时了解基础知识。

This section focuses on: How to get get going with AI 本节重点介绍： 如何开始使用 AI

1. Identifying needs 确定需求

Objectives 目标

  • Operational Efficiency: If your aim is to streamline operations, look for AI tools geared towards automation. 运营效率：如果您的目标是简化运营，请寻找面向自动化的 AI 工具。
  • Business Insights: If decision-making is your focus, analytics tools might be your go-to. 业务洞察：如果决策是您的重点，分析工具可能是您的首选。

Scope 范围

  • Focused Approach: Target a specific pain point, like customer support, and deploy a tailored AI solution like a chatbot. 重点方法：针对特定的痛点，如客户支持，并部署量身定制的人工智能解决方案，如聊天机器人。
  • Holistic Approach: Integrate AI across different business operations for an overarching impact. 整体方法：将 AI 集成到不同的业务运营中，以产生总体影响。

Budget 预算

  • Financial Budget: Determine the cost of implementing AI. 财务预算：确定实施 AI 的成本。
  • Human Resources: Do you have staff skilled in AI or will you need to hire or outsource? 人力资源：您是否有精通人工智能的员工，或者您是否需要雇用或外包？

2. Choosing tools 选择工具

Pre-built solutions 预构建的解决方案

  • For simple tasks, existing AI software like chatbots or recommendation engines can suffice. 对于简单的任务，现有的人工智能软件（如聊天机器人或推荐引擎）就足够了。

Custom solutions 定制解决方案

  • For specific needs, custom-built models are the way to go. 对于特定需求，定制模型是必经之路。

3. Getting started 入门

Plug-and-Play Services 即插即用服务

  • Platforms like Google’s AutoML offer easy-to-use AI solutions. Google 的 AutoML 等平台提供了易于使用的 AI 解决方案。

Code-Based Approaches 基于代码的方法

  • Use Python libraries like TensorFlow or PyTorch for more control over your AI model. 使用 TensorFlow 或 PyTorch 等 Python 库更好地控制 AI 模型。

API

  • Looking to build an AI-based app? You then need an AI-API Tool. 想要构建基于 AI 的应用程序？然后，您需要一个 AI-API 工具。

4. Data preparation 数据准备

Collection 收集

  • Gather the data the AI you create will learn from. 收集您创建的 AI 将从中学习的数据。

Cleaning 清洗

  • Remove or fix any inconsistencies or errors in your data. 删除或修复数据中的任何不一致或错误。

5. Model training 模型训练

Training Data 训练数据

  • Use a subset of your data to train your model. 使用数据子集来训练模型。

Validation and Testing 验证和测试

  • Use another subset to validate and test the model. 使用另一个子集来验证和测试模型。

Not sure how to do it? Read the guide on how to train ChatGPT, for example. 不知道该怎么做？例如，阅读有关如何训练 ChatGPT 的指南。

6. Go live 上线

Local Deployment 本地部署

  • Run your model on a local machine for testing or limited use. 在本地计算机上运行模型以进行测试或有限使用。

Cloud Deployment 云部署

  • Use cloud services for scalable, high-availability models. 将云服务用于可扩展的高可用性模型。

7. Maintenance 维护

Performance metrics 性能指标

  • Keep an eye on how well your model is performing. 密切关注模型的性能。

Updates 更新

  • Regularly update the model to adapt to new data. 定期更新模型以适应新数据。

8. Ethical concerns 道德问题

Data Privacy 数据隐私

  • Make sure you have the right permissions for the data you use. 确保您对使用的数据具有正确的权限。

Fairness and Bias 公平与偏见

  • Check your model doesn’t reinforce existing stereotypes or biases. 检查您的模型不会强化现有的刻板印象或偏见。

Chapter III: The 6 AI Key-Principles 第三章 人工智能的六大关键原则

This section emphasizes on: The 6 must-know AI principles. 本节重点介绍： 必须知道的 6 项 AI 原则。

Before you go into the real world and use AI… I need to make sure you’ve got the most important part. 在你进入现实世界并使用人工智能之前...... 我需要确保你拥有最重要的部分。

Below is my in-depth analysis + explanation. 以下是我的深入分析+解释。

1. Algorithms 算法

These are the brains behind Artificial Intelligence. 这些是人工智能背后的大脑。

  • Supervised Learning: Great for tasks where the answer is known, such as email filtering. 监督学习：非常适合已知答案的任务，例如电子邮件过滤。
  • Unsupervised Learning: Useful when you’re exploring data, like customer segmentation. 无监督学习：在探索数据（如客户细分）时很有用。
  • Reinforcement Learning: Ideal for decision-making processes, such as robotics. 强化学习：非常适合机器人等决策过程。

2. Data 数据

Data is the _fuel for AI. 数据是人工智能的燃料。

  • Structured Data: Think databases, where everything is categorized neatly. 结构化数据：想想数据库，其中所有内容都整齐地分类。
  • Unstructured Data: This is your texts, images, and videos. 非结构化数据：这是您的文本、图像和视频。
  • Real-Time Data: Crucial for applications requiring immediate decision-making, like autonomous vehicles. 实时数据：对于需要立即做出决策的应用至关重要，例如自动驾驶汽车。

3. Computational Power 计算能力

  • CPUs: Suitable for basic tasks but can be slower for complex models. CPU：适用于基本任务，但对于复杂模型可能较慢。
  • GPUs: A must-have for heavy lifting like deep learning. GPU：深度学习等繁重工作的必备工具。
  • Cloud Computing: Offers scalability, letting you pay as you go. 云计算：提供可扩展性，让您随用随付。

4. Interpretability 可解释性

  • Feature Importance: Know what variables are most influential in your model. 特征重要性：了解哪些变量对模型影响最大。
  • Explainable AI: Tools and models that allow you to understand how decisions are made. 可解释的 AI：可让您了解决策如何做出的工具和模型。
  • Bias Detection: Spot and correct any biased decisions made by the model. 偏差检测：发现并纠正模型做出的任何有偏差的决策。

5. Evaluation & Metrics 评估与指标

  • Accuracy: Measures the ratio of correct predictions to total predictions. But be cautious; it’s not always the best metric. 准确性：衡量正确预测与总预测的比率。但要小心;这并不总是最好的指标。
  • Precision and Recall: Useful for imbalanced datasets. Precision focuses on false positives, while recall focuses on false negatives. 精确度和召回率：适用于不平衡的数据集。精确度侧重于误报，而召回率侧重于漏报。
  • AUC-ROC Curve: Helpful for classification problems; it shows the trade-off between sensitivity and specificity. AUC-ROC曲线：有助于分类问题;它显示了敏感性和特异性之间的权衡。

6. OpenAI and ChatGPT

  • ChatGPT is: A conversational model that excels in generating human-like text, ideal for customer service bots, virtual assistants, and more. ChatGPT 是： 一种擅长生成类似人类文本的对话模型，非常适合客户服务机器人、虚拟助手等。
  • Fine-Tuning: OpenAI offers ways to fine-tune ChatGPT to meet specific requirements, enhancing its utility. 微调：OpenAI 提供了微调 ChatGPT 以满足特定要求的方法，从而增强了其实用性。
  • API Integration: With the OpenAI API, you can easily integrate ChatGPT into apps, websites, or other services. API 集成：使用 OpenAI API，您可以轻松地将 ChatGPT 集成到应用程序、网站或其他服务中。
  • Ethical Use: OpenAI provides guidelines on responsible use, emphasizing data privacy and unbiased responses. 道德使用：OpenAI 提供了负责任使用的指南，强调数据隐私和公正的回应。
  • Community and Updates: Staying updated with OpenAI’s research and community contributions can offer additional layers of mastery. 社区和更新：随时了解 OpenAI 的研究和社区贡献可以提供额外的掌握层次。

Relevant read: The 9 best AI-powered APIs 相关阅读： 9 个最佳 AI 驱动的 API

Chapter IV: Put AI Into Practice 第四章 人工智能的实践

This section is about: Integrating AI in your projects. 本节内容包括：将 AI 集成到项目中。

Now you know 100% about AI. Let’s apply that knowledge in the real world. 现在你对人工智能有100%的了解。 让我们将这些知识应用到现实世界中。

1. Project planning 项目规划

Set the stage first. 首先搭建平台。

  • Scope Definition: Clearly outline what you aim to achieve with AI. 范围定义：清楚地概述您打算通过 AI 实现的目标。
  • Milestones: Set key milestones for tracking progress. 里程碑：设置用于跟踪进度的关键里程碑。
  • Risk Assessment: Identify potential challenges and how to mitigate them. 风险评估：识别潜在挑战以及如何缓解这些挑战。

2. Team building 团队建设

  • Domain Experts: Include people who understand the industry you’re working in. 领域专家：包括了解您所从事的行业的人。
  • Data Scientists: The core team who will build and fine-tune your models. 数据科学家：负责构建和微调模型的核心团队。
  • Software Engineers: Those who’ll integrate the model into your existing systems. 软件工程师：将模型集成到现有系统中的人员。

3. Foundation 基础

First, tools and infrastructure. 首先是工具和基础设施。

  • Development Environment: Set up IDEs and repositories for code storage. 开发环境：设置用于代码存储的 IDE 和存储库。
  • Data Storage: Decide on databases and data lakes. 数据存储：确定数据库和数据湖。
  • Computational Resources: Secure necessary CPUs, GPUs, or cloud services. 计算资源：保护必要的 CPU、GPU 或云服务。

4. Blueprint 蓝图

  • Model Selection: Choose initial models to test, based on your needs. 模型选择：根据需要选择要测试的初始模型。
  • Data Sampling: Use a small sample of data to validate the model’s concept. 数据抽样：使用少量数据样本来验证模型的概念。
  • Feedback Loop: Create a mechanism for continuous feedback and model tweaking. 反馈循环：创建持续反馈和模型调整机制。

5. Integrate AI 集成 AI

  • Integration Testing: Make sure the model interacts well with other system components. 集成测试：确保模型与其他系统组件的交互良好。
  • Deployment: Roll out the model, initially perhaps as a beta version. 部署：推出模型，最初可能作为测试版。
  • User Training: Educate the end-users on how to interact with the new AI features. 用户培训：教育最终用户如何与新的 AI 功能进行交互。

6. Analytics & Feedback 分析与反馈

  • Dashboard: Implement real-time tracking of key performance indicators (KPIs). 仪表板：实现关键绩效指标 （KPI） 的实时跟踪。
  • Quality Assurance: Continuously validate the model’s output. 质量保证：持续验证模型的输出。
  • Iterative Updates: Re-train the model as more data comes in or as objectives change. 迭代更新：随着更多数据的传入或目标的变化，重新训练模型。

7. Review your work 评审你的工作

  • Success Metrics: Did the project meet or exceed the predefined KPIs? 成功指标：项目是否达到或超过预定义的 KPI？
  • Lessons Learned: What worked well, and what would you do differently? 经验教训：哪些方面效果很好，你会做些什么不同的事情？
  • Future Planning: Identify new opportunities for further AI implementation. 未来规划：确定进一步实施 AI 的新机会。

Chapter V: Scale Your AI Efforts 第五章：扩展 AI 工作

This section is about: Making more with AI out of what you already have. 本节是关于：利用您已经拥有的东西，利用 AI 创造更多收益。

Happy or not with your results, there’s always room for improvement. 无论您对结果满意与否，总有改进的余地。

1. User adoption 用户采用

More users, more data! 更多用户，更多数据！

  • Onboarding Plans: Craft easy-to-follow guides for new users. 入职计划：为新用户制作易于遵循的指南。
  • Feedback Channels: Collect user insights to understand adoption rates and bottlenecks. 反馈渠道：收集用户见解以了解采用率和瓶颈。

2. Diversification 多元化

This is for more ways to use AI. 这是为了使用人工智能的更多方法。

  • New Use Cases: Identify additional tasks or decisions where AI could be beneficial. 新用例：确定 AI 可能有益的其他任务或决策。
  • Feature Additions: Consider adding new features to your existing AI tools to make them more versatile. 功能添加：考虑向现有 AI 工具添加新功能，使其更加通用。

3. Cost optimization 成本优化

  • Tiered Plans: If you’re using subscription services, adjust plans according to usage. 分层计划：如果您使用的是订阅服务，请根据使用情况调整计划。
  • Cost-Effectiveness Metrics: Continually assess the ROI of your AI implementations. 成本效益指标：持续评估 AI 实施的投资回报率。

4. Service improvements 服务改进

  • Fine-Tuning: Make small adjustments to your AI services to improve performance. 微调：对 AI 服务进行小幅调整以提高性能。
  • Speed and Responsiveness: Ensure your AI tools are as quick and agile as they need to be. 速度和响应能力：确保您的 AI 工具尽可能快速和敏捷。

5. Data governance 数据治理

  • Privacy Policies: Make sure your AI usage complies with local and international data laws. 隐私政策：确保您的 AI 使用符合当地和国际数据法律。
  • Quality Assurance: Keep tabs on data quality and integrity, especially as you scale. 质量保证：密切关注数据质量和完整性，尤其是在扩展时。

6. Skill building 技能培养

  • Training Modules: As AI evolves, so should your or your team’s understanding of it. 培训模块：随着人工智能的发展，您或您的团队对它的理解也应该如此。
  • Certifications: Obtain industry-recognized credentials for mastered AI platforms or methodologies. 认证：获得行业认可的掌握 AI 平台或方法的证书。

7. Vendor management 供应商管理

  • Service Level Agreements (SLAs): Revisit these to make sure vendors are meeting your scaled needs. 服务水平协议 （SLA）：重新审视这些协议，以确保供应商满足您的扩展需求。
  • Multi-Vendor Strategies: Don’t put all your eggs in one basket; diversify your AI sources. 多供应商策略：不要把所有的鸡蛋都放在一个篮子里;使您的 AI 来源多样化。

8. Engage with people 与人互动

  • Forums and Groups: Engage in discussions to stay updated on the latest AI trends and best practices. 论坛和小组：参与讨论，随时了解最新的 AI 趋势和最佳实践。
  • Webinars and Events: These can offer new perspectives and deepen your understanding of AI applications. 网络研讨会和活动：这些可以提供新的视角并加深您对 AI 应用的理解。

Scaling your AI efforts is about optimizing across various fronts: cost, performance, and skills. It’s not just about doing more, but doing better. 扩展 AI 工作涉及在各个方面进行优化：成本、性能和技能。 这不仅仅是要做得更多，而是要做得更好。

Chapter VI: Keep Up with Trends 第六章 紧跟潮流

This chapter gives you: Tools and resources to keep your finger on the pulse. 本章为您提供： 掌握脉搏的工具和资源。

Staying updated is essential in the fast-paced world of AI. From formal education to social media scrolling, every bit counts.在快节奏的人工智能世界中，保持更新至关重要。从正规教育到社交媒体滚动，每一点都很重要。

1. News 新闻

  • Subscriptions: Sign up for newsletters from reputable AI sources. 订阅：注册来自信誉良好的 AI 来源的时事通讯。
  • Example: MIT Technology Review. 示例：《麻省理工科技评论》。

2. Academic sorces 学术联谊会

  • Database Access: Utilize platforms like Google Scholar or JSTOR for the latest research. 数据库访问：利用 Google Scholar 或 JSTOR 等平台进行最新研究。
  • Example: Journal of Artificial Intelligence Research. 示例：Journal of Artificial Intelligence Research。

3. Social media 社交媒体

  • Follow Influencers: Key people in the AI field often share valuable insights. 关注有影响力的人：人工智能领域的关键人物经常分享有价值的见解。
  • Examples: Andrew Ng on Twitter, Yann LeCun on LinkedIn. 例如：Andrew Ng在Twitter上，Yann LeCun在LinkedIn上。

4. Conferences & Webinars 会议和网络研讨会

  • Calendar Alerts: Set reminders for big annual events you don’t want to miss. 日历提醒：为您不想错过的年度大型活动设置提醒。
  • Example: NeurIPS.示例：NeurIPS。

5. Courses 课程

  • Continued Learning: New methods and tools are always emerging. Stay current. 持续学习：新的方法和工具不断涌现。保持最新状态。
  • Example: Coursera’s Machine Learning by Andrew Ng. 示例：Andrew Ng 的 Coursera 机器学习。

6. Podcasts & Videos 播客和视频

  • Curated Lists: Create playlists of shows or channels that consistently deliver quality content. 精选列表：创建始终如一地提供优质内容的节目或频道的播放列表。
  • Examples: Two Minute Papers on YouTube. 示例：YouTube 上的两分钟论文。

7. Networking 网络

  • Communities and Groups: Join AI-focused groups on LinkedIn or specialized forums. 社区和群组：加入 LinkedIn 或专业论坛上以 AI 为重点的群组。
  • Example: r/MachineLearning. 示例：r/MachineLearning。

8. Case studies 案例研究

  • Corporate Insights: Companies often release whitepapers explaining how they solved problems using AI. 企业洞察：公司经常发布白皮书，解释他们如何使用人工智能解决问题。
  • Example: Google AI Blog.示例：Google AI 博客。

9. Open-source projects 开源项目

  • Contribution: Taking part in these projects can give you practical experience and insights. 贡献：参与这些项目可以给你带来实践经验和见解。
  • Example: TensorFlow on GitHub. 示例：GitHub 上的 TensorFlow。

Conclusion结论

I hope this guide helped you gain more knowledge on AI. Learn how to become more productive with our guides on how to use AI. Thank you for reading this
我希望本指南能帮助您获得更多有关 AI 的知识。 通过我们关于如何使用 AI 的指南，了解如何提高工作效率。 感谢您阅读本文

原文：https://guides.ai/how-to-get-into-ai