├── .github
└── ISSUE_TEMPLATE
│ ├── bug.yml
│ ├── feedback.yml
│ └── review.yml
├── Chapters
├── Economics
│ └── test.md
├── People
│ └── test.md
├── Processes
│ ├── AI-Compass A Framework for Identifying High-ROI AI Use Cases.pdf
│ └── test.md
└── Technology
│ ├── AI Capability Continuum.pdf
│ └── test.md
├── Docs
├── Introduction.md
├── Why Another AI Book.md
└── test.md
├── LICENSE
├── README.md
└── images
├── 2.jpg
└── 3.jpg
/.github/ISSUE_TEMPLATE/bug.yml:
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1 | name: 🐛 Bug Report
2 | description: Report typos, broken links, or errors in the book
3 | title: "[Bug]: "
4 | labels: ["bug"]
5 | body:
6 | - type: textarea
7 | id: bug-description
8 | attributes:
9 | label: "Describe the issue"
10 | description: "Explain what’s wrong (e.g., typo, incorrect information, broken link)."
11 | placeholder: "Describe the bug here..."
12 | validations:
13 | required: true
14 | - type: input
15 | id: chapter
16 | attributes:
17 | label: "Which chapter/section has this issue?"
18 | description: "Mention the chapter or section number if applicable."
19 | placeholder: "e.g., Chapter 5 - Neural Networks"
20 | - type: textarea
21 | id: suggested-fix
22 | attributes:
23 | label: "Suggested Fix (Optional)"
24 | description: "If you have a suggestion for fixing this issue, describe it here."
25 | placeholder: "Suggested fix or correction..."
26 |
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/.github/ISSUE_TEMPLATE/feedback.yml:
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1 | name: 📢 Feedback
2 | description: Provide feedback on the book – clarity, structure, or anything else!
3 | title: "[Feedback]: "
4 | labels: ["feedback"]
5 | body:
6 | - type: textarea
7 | id: feedback
8 | attributes:
9 | label: "What feedback do you have?"
10 | description: "Tell us what you think! Is something unclear, missing, or could be improved?"
11 | placeholder: "Describe your feedback here..."
12 | validations:
13 | required: true
14 | - type: input
15 | id: chapter
16 | attributes:
17 | label: "Which chapter/section is this about?"
18 | description: "Mention the chapter or section number if applicable."
19 | placeholder: "e.g., Chapter 3 - AI Ethics"
20 | - type: textarea
21 | id: additional-comments
22 | attributes:
23 | label: "Any additional comments?"
24 | description: "Anything else you’d like to share?"
25 | placeholder: "Additional thoughts or suggestions..."
26 |
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/.github/ISSUE_TEMPLATE/review.yml:
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1 | name: ⭐ Book Review
2 | description: Rate and review the book on different aspects
3 | title: "[Review]: "
4 | labels: ["review", "feedback"]
5 | body:
6 | - type: markdown
7 | attributes:
8 | value: "Thank you for taking the time to review the book! Please rate each category from **1 (Needs Improvement) to 5 (Excellent)**."
9 |
10 | - type: dropdown
11 | id: clarity
12 | attributes:
13 | label: 📖 Clarity of Explanation
14 | description: "Was the content easy to understand?"
15 | options:
16 | - "1 - Hard to follow"
17 | - "2 - Somewhat unclear"
18 | - "3 - Neutral"
19 | - "4 - Mostly clear"
20 | - "5 - Very clear"
21 | validations:
22 | required: true
23 |
24 | - type: dropdown
25 | id: structure
26 | attributes:
27 | label: 📚 Book Structure & Flow
28 | description: "Was the organization of topics logical and easy to follow?"
29 | options:
30 | - "1 - Very disorganized"
31 | - "2 - Somewhat unclear"
32 | - "3 - Neutral"
33 | - "4 - Well-structured"
34 | - "5 - Excellent flow"
35 | validations:
36 | required: true
37 |
38 | - type: dropdown
39 | id: depth
40 | attributes:
41 | label: 🔍 Depth of Content
42 | description: "Did the book provide enough depth and insights?"
43 | options:
44 | - "1 - Too shallow"
45 | - "2 - Needs more detail"
46 | - "3 - Balanced"
47 | - "4 - Detailed"
48 | - "5 - Very in-depth"
49 | validations:
50 | required: true
51 |
52 | - type: dropdown
53 | id: engagement
54 | attributes:
55 | label: ✨ Engagement & Readability
56 | description: "Was the book engaging and enjoyable to read?"
57 | options:
58 | - "1 - Boring"
59 | - "2 - Somewhat dull"
60 | - "3 - Neutral"
61 | - "4 - Mostly engaging"
62 | - "5 - Very engaging"
63 | validations:
64 | required: true
65 |
66 | - type: dropdown
67 | id: usefulness
68 | attributes:
69 | label: 🎯 Practical Usefulness
70 | description: "Did you find the content applicable and useful?"
71 | options:
72 | - "1 - Not useful"
73 | - "2 - Slightly useful"
74 | - "3 - Neutral"
75 | - "4 - Mostly useful"
76 | - "5 - Very useful"
77 | validations:
78 | required: true
79 |
80 | - type: textarea
81 | id: additional-comments
82 | attributes:
83 | label: "📝 Additional Comments"
84 | description: "Any other feedback or suggestions?"
85 | placeholder: "Write here..."
86 |
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/Docs/Introduction.md:
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1 | Back in the late 90s when the Internet had just come, 99% of the Organizations saw Internet as just another channel of distribution. 1% of the companies saw the internet as the business itself. It is these 1% - Google, Amazon, Facebook, Apple etc that went on to create massive wealth and made the most of once in a lifetime opportunity. AI is at a similar junction. It is not just another technology. 99% of the Organizations see it as a great way to optimize their manpower & operations (which will surely help them improve their bottomline). But that not the true power of this technology. The true power of it lies in reimagining one entire business in the light of this technology. Only 1% of the companies are doing so. But it is this 1% that is going make the most of AI potential - the next set of unicorns, decacorns will come from these 1% and not the 99%.
2 |
3 | This is best summarised by Satya Nadella when he says "The core currency of any business will be its ability to harness AI and fundamentally reimagine what it does & how it operates in the light of AI."
4 |
5 | Mark Cuban famously said "There are only two types of companies in this world - Those that are great at AI and everybody else. If you are not good at AI you are going to fail. Period. End of story. And if you are a CEO, you can’t just leave it to the tech guys". He puts the onus of AI transformation directly on the CEO & his entire leadership team and not just the CTO or CIO. Why so? because unlike IT, AI is not just a better way of doing things. Its an Organization wide initative and starts with CXO team taking bold bets and reimagining their entire business in the light of this technology.
6 |
7 | It is owing to this promise, every team, organization, country is after mastering this technology. Head of States are conference chairs! Billions of dollars are being invested in this technology around the world and trillions more will be invested.
8 |
9 |
10 |
--------------------------------------------------------------------------------
/Docs/Why Another AI Book.md:
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1 | In today’s rapidly evolving AI landscape, (Generative) AI promises to revolutionize businesses like never before. While it unlocks unparalleled opportunities, it also brings complex challenges in turning this disruptive technology into successful AI features, products & ventures. The key is to unlock these opportunities is to elevate your organization's AI practices toward "Pragmatic AI"—where AI's promise meets real-world impact.
2 |
3 | We define Pragmatic AI as AI efforts that translate into tangible business successes, leading to increased revenues and market dominance, rather than focusing solely on model metrics.
4 |
5 | "AI First by Design" is your guide to Pragmatic AI. It is a practical resource for Executives, Leaders, Startup Founders, Partners at VC/PE firms, Managers and Builders, focused on running AI initiatives that deliver real business impact.
6 |
7 |
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/LICENSE:
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1 | Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
2 |
3 | This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.
4 | To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/.
5 |
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/README.md:
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1 |
2 | [](https://github.com/janishar/mit-deep-learning-book-pdf/raw/master/complete-book-pdf/deeplearningbook.pdf)
3 | [](https://github.com/janishar/mit-deep-learning-book-pdf/blob/master/complete-book-pdf/Ian%20Goodfellow%2C%20Yoshua%20Bengio%2C%20Aaron%20Courville%20-%20Deep%20Learning%20(2017%2C%20MIT).pdf)
4 |
5 | # **AI First by Design**
6 | ### Your Guide to Accelerating AI Maturity & Leading High-Performance AI Initiatives & Organizations
7 | by
8 | [Anuj Gupta](https://www.linkedin.com/in/anujgupta-82/)
9 |
10 |
11 | 
12 | 
13 |
14 |
15 |
16 |
17 | -----------------------------------------------------------------------------------
18 |
19 |
20 | Why another book on AI
21 |
22 | In today’s rapidly evolving AI landscape, (Generative) AI promises to revolutionize businesses like never before. While it unlocks unparalleled opportunities, it also brings complex challenges in turning this disruptive technology into successful AI features, products & ventures. The key to unlock these opportunities is to elevate your organization's AI practices toward "Pragmatic AI"— AI efforts that deliver real-world impact.
23 |
24 | We define **Pragmatic AI** as _AI efforts that translate into tangible business successes, leading to increased revenues and market dominance, rather than focusing solely on model metrics_.
25 |
26 | "AI First by Design" is your guide to Pragmatic AI. It is a practical resource for Executives, Leaders, Startup Founders, Partners at VC/PE firms, Managers, Engineers, Scientist focused on being part of AI initiatives that deliver real business impact.
27 |
28 |
29 |
30 |
31 | -----------------------------------------------------------------------------------
32 |
33 |
34 |
35 | Introduction
36 |
37 | Artificial Intelligence is no longer a futuristic concept—it’s the defining force shaping the next era of business, technology, and innovation. Yet, despite the hype, many organizations struggle to build AI systems that drive real impact. AI projects often stall due to unclear objectives, poor alignment with business goals, talent gaps, or an underdeveloped AI strategy.
38 |
39 | This playbook is designed to change that. It’s a no-nonsense, practical guide for leaders, Startup founders, and AI practitioners who want to build world-class AI systems, products, and teams—not just PoCs (Proof of Concepts) or prototypes that never make it to production. Whether you’re a startup founder, corporate executive, or VC partner, this playbook will help you navigate the complexities of AI adoption and execution.
40 |
41 | We distill lessons from over two decades of hands-on AI experience—spanning early-stage startups (0-1, 1-n) to Fortune 50 giants. Having led numerous high-impact AI initiatives, we’ve curated the most valuable insights into a strategic, results-driven approach for building AI systems that truly move the needle.
42 |
43 | This playbook serves as both a roadmap for AI adoption and a blueprint for success. It dives into the key parameters of AI readiness, addressing why AI development fundamentally differs from traditional IT/software systems and why the known playbooks for Software 1.0 fall short when building Software 2.0 (AI-driven systems).
44 |
45 | Understanding these principles is crucial for AI founders, executives, CXOs, VC partners, and senior policymakers. Organizations with leaders who grasp these concepts will achieve higher AI maturity, readiness, and a distinct competitive edge.
46 |
47 | Our hope is that this playbook becomes an invaluable resource for AI startup founders, MNC executives, AI scientists, developers, investors, and policymakers driving AI-led transformation worldwide. This isn’t just another AI book—it’s a playbook for action. By the end, you’ll have a clear roadmap to build and scale AI systems that deliver tangible business impact, innovation, and lasting competitive advantage.
48 |
49 |
50 |
51 | -----------------------------------------------------------------------------------
52 |
53 |
54 |
55 | Once in a Lifetime Opportunity
56 |
57 | Back in the late 90s when Internet had just come, 99% of the Organizations saw Internet as just another channel of distribution. 1% of the companies saw the internet as the business itself. It is these 1% - Google, Amazon, Facebook, Apple etc that went on to create massive wealth and made the most of once in a lifetime opportunity. AI is at a similar junction. It is not just another technology. 99% of the Organizations see it as a great way to optimize their manpower & operations (which will surely help them improve their bottomline). But that not the true power of this technology. The true power of it lies in reimagining one entire business in the light of this technology. Only 1% of the companies are doing so. But it is this 1% that is going make the most of AI potential - the next set of unicorns, decacorns will come from these 1% and not the 99%.
58 |
59 | This is best summarised by Satya Nadella when he says "The core currency of any business will be its ability to harness AI and fundamentally reimagine what it does & how it operates in the light of AI."
60 |
61 | Mark Cuban famously said "There are only two types of companies in this world - Those that are great at AI and everybody else. If you are not good at AI you are going to fail. Period. End of story. And if you are a CEO, you can’t just leave it to the tech guys". He puts the onus of AI transformation directly on the CEO & his entire leadership team and not just the CTO or CIO. Why so? because unlike IT, AI is not just a better way of doing things. Its an Organization wide initative and starts with CXO team taking bold bets and reimagining their entire business in the light of this technology.
62 |
63 | It is owing to this promise, every team, organization, country is after mastering this technology. Head of States are conference chairs! Billions of dollars are being invested in this technology around the world and trillions more will be invested.
64 |
65 |
66 |
67 | -----------------------------------------------------------------------------------
68 |
69 |
70 | The Central Problem with today's AI Initiatives
71 |
72 |
73 |
74 | -----------------------------------------------------------------------------------
75 |
76 |
77 | The Fundamental Mistake
78 |
79 |
80 |
81 | -----------------------------------------------------------------------------------
82 |
83 |
84 | Common Sense Is All We Need
85 |
86 |
87 |
88 | -----------------------------------------------------------------------------------
89 |
98 |
99 |
100 |
101 |
102 | Table of Contents
103 |
104 |
105 |
106 | ### Section 1: Introduction
107 | - **Ch 1:** Once in a Lifetime Opportunity
108 | - **Ch 2:** The Fundamental Mistake
109 |
110 | ### Section 2: Technology
111 | - **Ch 3:** Human Brain Analogy is Super Misleading
112 | - Incremental Learning
113 | - Explainability
114 | - **Ch 4:** AI ≠ Replace Humans?
115 | - AI = Augmented Intelligence rather than Artificial Intelligence
116 | - **Ch 5:** The Right Methodology to Develop AI Systems
117 | - [Capability Curve for AI Systems vs IT Systems](https://drive.google.com/file/d/1B6S2X9utAConHYCN871_iBFIR2xhFLIY/view?usp=drive_link)
118 | - Make it Work, Make it Better
119 | - **Ch 6:** AI Won’t Be Error-Free Anytime Soon
120 | - Mistakes are Part and Parcel of AI Systems
121 | - **Ch 7:** AI Problems Can Be Solved in Multiple Ways
122 | - AI Teams Do Not Know the Right Path Upfront
123 | - Finding the Path Involves Many Experiments
124 | - Solutions Differ Based on the Problem and Dataset
125 | - **Ch 8:** AI Solutions Are Not Incremental
126 | - Going from 85% to 90% Accuracy May Require Starting Over
127 | - **Ch 9:** Updating AI Models ≠ Old Learnings + New Learnings
128 | - Can Lead to:
129 | - Incorrect Predictions on Previously Correct Data Points
130 | - Catastrophic Forgetting
131 | - **Ch 10:** AI Model Quality Mirrors Training Data Quality
132 | - Unseen Data Examples
133 | - **Ch 11:** Drift
134 | - Data Drift vs Concept Drift
135 | - **Ch 12:** Quantifying Data Needs is Hard
136 | - Deep Learning is a Data Guzzler
137 | - Transfer Learning Doesn’t Work in All Scenarios
138 | - **Ch 13:** Data Quality Matters
139 | - Can't Run Fighter Jets on Crude Oil!
140 | - Quantifying the Impact of Bad Data is Tough
141 | - **Ch 14:** AI Ops is Not DevOps
142 | - Why Deploying and Maintaining Models is Much More Nuanced
143 |
144 | ### Section 3: Processes
145 | - **Ch 15:** AI is Not a Sledgehammer
146 | - [Framework for finding most appropriate AI Use Cases](https://drive.google.com/file/d/1LH80juojBUUMsK9ujXeMVX19oLtsVaD7/view?usp=drive_link)
147 | - **Ch 16:** Defining the Right Metrics to Measure Success
148 | - AI Metrics
149 | - Business Metrics
150 | - Synchrony Between AI & Business Metrics
151 | - **Ch 17:** Data Labeling
152 | - AI is Only as Good as Its Data Labelers
153 | - **Ch 18:** Testing AI Systems
154 | - “Incorrect” Output
155 | - **Ch 19:** Agile is Unfit for Managing AI Development
156 | - AI Development is Highly Unpredictable
157 | - **Ch 20:** Data Collection
158 | - Building Nuclear Systems Requires Rigorous Data Collection, Cleaning, and Enrichment
159 | - **Ch 21:** The Safety Net of AI Systems
160 | - Control Loop
161 | - Feedback Loop
162 | - Human in the Loop
163 | - UX for AI
164 | - Handling Errors and Failures Gracefully
165 | - Introducing AI to Users Gradually
166 | - With AI, Always Better to Under-Promise than Over-Promise
167 | - **Ch 22:** Explainability and Trust
168 |
169 | ### Section 4: Economics of AI
170 | - **Ch 23:** The Business of AI is Far from SaaS
171 | - Why Profit Margins in AI (~40%) are Much Lower than SaaS (~85%)
172 | - **Ch 24:** Why AI Endeavors Are Still Expensive
173 | - **Ch 25:** Crucial to Quickly Find the Market's Price for AI
174 | - Customers Pay for Value, Not AI
175 | - **Ch 26:** Accuracy Obsession Early On is Counterproductive
176 | - When is Improving AI Systems Incrementally (1-2%) Worth It?
177 | - **Ch 27:** How is GPU Cost Likely to Play Out in the Future?
178 |
179 | ### Section 5: People
180 | - **Ch 28:** Stakeholder Management
181 | - Do You Even Need AI?
182 | - Ill-Formed Problems
183 | - Capability Curve for AI Systems
184 | - Make it Work, Make it Better
185 | - Who is the Best Person to Manage Stakeholders?
186 | - **Ch 29:** The Right Hiring Strategy for AI
187 | - Generalist vs Specialist
188 | - Does Hiring PhDs Give You an Edge?
189 | - When is the Right Time to Hire a Head of AI?
190 | - What Should the Head of AI Be—PhD or MBA?
191 | - Bilingual AI Leaders
192 | - **Ch 30:** Managing Individual Contributors (ICs)
193 | - Occam's Razor
194 | - Falling in Love with the Solution, Not the Problem
195 | - AI System >> AI Model
196 | - Clues Lie in Mistakes
197 | - **Ch 31:** AI Product Manager: The Underrated Ace
198 | - **Ch 32:** Publishing
199 |
200 |
201 |
202 | -----------------------------------------------------------------------------------
203 |
204 |
205 | About The Author
206 |
207 | **What I do:**
208 |
209 | - On-demand Head of (Gen) AI to multiple Startups & MNCs across US, Europe & India.
210 | - AI Advisor to multiple Boards, mentoring them on various facets of (Gen) AI.
211 | - Helping MNCs put together a strong (Gen) AI strategy & roadmap in place
212 | - Conduct Highly bespoke workshops on (Gen) AI for CXOs, Executives, Board Members, VC/PE partners.
213 | - Mentor Global Capability Centers (GCCs) in setting up AI Center of Excellence (CoE)
214 |
215 | **My background:**
216 |
217 | - Seasoned AI executive with 20+ years of extensive experience in spearheading core AI efforts, driving AI KPIs as Chief AI Officer.
218 | - Recently mentored a YC company build critical AI systems, demoed to [Sam Altman](https://en.wikipedia.org/wiki/Sam_Altman) (Open AI) & [Vinod Khosla](https://www.khoslaventures.com/team/vinod-khosla/) (Khosla Ventures); helping YC company secure series B funding by Khosla Ventures.
219 | - Led AI efforts for one of the earliest startups to be funded for its AI-first approach, back in 2013;
220 | leading to its acquisition by FreshWorks (Nasdaq listed) in 2016.
221 | - Authored a landmark book in AI. The book has been [endorsed by Globally Top AI Leaders](https://www.practicalnlp.ai/testimonial#testimonials) by from CMU, UCSD, DeepMind, Google AI, Microsoft Research, Amazon AI Research, Meta, Spotify, YC startups including Airbnb & Sharpest Minds.
222 |
223 | The book has already been translated in 5 languages, [270+ citations](https://scholar.google.com/scholar?oi=bibs&hl=en&cites=4105469700075134896&as_sdt=5), and used by 50+ universities globally for their AI curriculum. Ranks consistently in the top 1% of AI books.
224 |
225 | Presented our book to [Prof Raj Reddy](https://en.wikipedia.org/wiki/Raj_Reddy) (**Turing award winner, doyen of AI & Robotics at CMU**) and [Dr Srinivas Bangalore](https://sbangalore.com/) (SVP of AI at Interactions Corporation. Visiting professor at Columbia University, Princeton University, and Copenhagen Business School)
226 |
227 | - Incubated & spearheaded AI efforts at both startups (0-1, 1-n) as well as Fortune 50 companies. Developed commercially
228 | successful AI products and features leading to multi-million $$ in revenue & contributing significantly to Org KPIs.
229 |
230 | - Incubated & led AI teams of size 5-100 people spanning multiple geographies, managing the entire team & lifecycle of AI projects.
231 |
232 | - Incubated & led AI efforts & built multiple AI systems in NLP, Vision, Speech and Data Science at both
233 | startups (0-1, 1-n) & Fortune 50.
234 |
235 | - Worked very closely with Founders and my C-suite peers across Business, Product, Engineering, Sales, and HR to drive KPIs as Chief AI Officer at the organizational level.
236 |
237 |
238 |
239 | You can connect with me here:
240 | * [Linkedin](https://www.linkedin.com/in/anujgupta-82/)
241 | * [Twitter](https://twitter.com/anujgupta82)
242 | * [YouTube Channel](https://www.youtube.com/@gradient_advisors)
243 |
244 |
245 |
246 | -----------------------------------------------------------------------------------
247 |
248 |
249 | Dont agree with a topic, point of view
250 |
251 |
252 |
253 | -----------------------------------------------------------------------------------
254 |
255 |
256 | You have better Example, Feedback, Suggestion or just want to start a discussion
257 |
258 | Great, please head over to the [discussion]() section
259 |
260 |
261 |
262 | -----------------------------------------------------------------------------------
263 |
264 |
265 |
266 | Found a Typo, Spelling Mistake, or Bug
267 |
268 | Despite our best efforts to eliminate errors, some typos, spelling mistakes, or bugs may still slip through.
269 | If you encounter any, please log them using the [Issues tab](https://github.com/anujgupta82/AI-by-Design/issues).
270 |
271 | Your feedback will help us fix them and enhance the overall quality of the book. Thank you for your support!
272 |
273 |
274 |
275 | -----------------------------------------------------------------------------------
276 |
277 |
278 | Citing any material from this book
279 |
280 | ### Citing any material from this book
281 | As you use this work or any part of it, please cite it properly.**
282 |
283 | ```
284 | To cite this book or any portion of this book, please use this bibtex entry:
285 |
286 | @book{Gupta-2025,
287 | title={AI First by Design},
288 | author={Anuj Gupta},
289 | note={\url{[www.anujgupta.co/AI-first-by-design}},
290 | year={2025}
291 | }
292 | ```
293 |
294 |
295 |
296 | -----------------------------------------------------------------------------------
297 |
298 | 🌟 Support this work
299 |
300 | If this work helps you or enriches your knowledge in any way, please show your love :heart: by putting a :star: on this project :v:. Please support us by writing about this work on various social forums and share it with your network to show your appreciation and help others discover it.
301 |
302 | [](https://github.com/AI-First-by-Design/AI-First-by-Design-Book/stargazers)
303 | [](https://twitter.com/intent/tweet?text=Check%20out%20this%20awesome%20AI%20resource%20by%20%40anujgupta82%20from%20%40GradientAdvisor%3A%20https%3A%2F%2Fgithub.com%2FAI-First-by-Design%2FAI-First-by-Design-Book)
304 | [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fgithub.com%2FAI-First-by-Design%2FAI-First-by-Design-Book)
305 |
306 |
307 | Feel free to tag our handles:
308 |
309 | X / Twitter: [@anujgupta82](https://X.com/anujgupta82) [@gradientAdvisor](https://x.com/GradientAdvisor)
310 |
311 | Linkedin: [anujgupta-82](https://www.linkedin.com/in/anujgupta-82/) [gradient-advisors](https://www.linkedin.com/company/gradient-advisors/)
312 |
313 | YouTube: [@gradient_advisors](https://www.youtube.com/@gradient_advisors)
314 |
315 | Discord:
316 |
317 | Website: [Gradient Advisors](https://gradient-advisors.ai/)
318 |
319 |
320 |
321 | -----------------------------------------------------------------------------------
322 |
323 | License
324 |
325 | [](http://creativecommons.org/licenses/by-nc/4.0/)
326 |
327 |
328 |
329 |
339 |
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