*Uses of AI and data science can be found in almost any situation that produces data
* More uses for custom AI applications and data-derived
insights than for traditional software engineering
* Literacy in AI-oriented coding will be more valuable than traditional coding
ChatGPT (Chat Generative pre-defined transformer) is OpenAI's application that performs human like interactions. GitHub Copilot uses the OpenAI Codex to suggest code and entire functions in real-time, right from your editor. Deck contains more details about ChatGPT, AI, AGI, CoPilot, OpenAI API, and use case scenarios.
Data Science methods and case studies in anomaly detection - from SPC to Deep Learning Autoencoders and signature analysis. Includes application of models on event streams. Case study - IoT sensor data from energy production facilities.
ChatGPT (Chat Generative pre-defined transformer) is OpenAI's application that performs human like interactions. GitHub Copilot uses the OpenAI Codex to suggest code and entire functions in real-time, right from your editor. Deck contains more details about ChatGPT, AI, AGI, CoPilot, OpenAI API, and use case scenarios.
Data Science methods and case studies in anomaly detection - from SPC to Deep Learning Autoencoders and signature analysis. Includes application of models on event streams. Case study - IoT sensor data from energy production facilities.
Python, the Language of Science and Engineering for EngineersBoey Pak Cheong
A talk given in November 2016 at IEM Malaysia to engineers, who are new to Python, a broad perspective of what Python is, why it is important to learn it and how it can help in solving/visualization of engineering and scientific tasks and problems.
Presented at All Things Open RTP Meetup
Presented by Brent Laster
Title: Introduction to GitHub Copilot
Abstract: Learn about the future of coding with this presentation, "Introduction to GitHub Copilot." Join author, speaker, and DevOps director Brent Laster as we discuss the GitHub coding assistant that leverages the power of artificial intelligence to augment your development activities. In this session, we'll delve into the core features of GitHub Copilot, explaining what it is, and learning about its ability to generate code snippets, autocompletions, and even entire functions across a variety of programming languages.
Learn how Copilot enhances your coding efficiency by contextualizing suggestions based on your codebase and offering a seamless integration into popular code editors. We'll explore some example applications and look at how developers can harness Copilot to streamline their workflows and boost productivity. Whether you're a seasoned developer or just embarking on your coding journey, this presentation offers a quick introduction to GitHub Copilot's capabilities, as a starting point for you to embrace cutting-edge AI assistance in your coding endeavors.
Youtube Link: https://youtu.be/woVJ4N5nl_s
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka PPT on 'Python Basics' will help you understand what exactly makes Python special and covers all the basics of Python programming along with examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
How do OpenAI GPT Models Work - Misconceptions and Tips for DevelopersIvo Andreev
Have you ever wondered why GPT models work? Do you ask questions like:
◉ How does GPT work? Why does the same problem receive different answers for different users? Is there a way to improve explainability? ◉ Can GPT model provide its sources? Why does Bing chat work differently? What are my ways to have better performance and improve completions? ◉ How can I work with data in my enterprise? What practical business cases could a generative AI model fit solving?
If you are tired of sessions just scratching the surface of OpenAI GPT, this one will go deeper and answer questions like why, why not and how.
Key Terms; ChatGPT Enterprise; Top Questions; Enterprise Data; Azure Search; Functions; Embeddings; Context Encoding; General Intelligence; Emerging Abilities; Chain of Thought; Plugins; Multimodal with DALL-E; Project Florence
POWERED BY -- teachersduniya.com
This is the based on python programming language which is the very power full language . you can also learn whole about overview python.
Powerful tools give a lot room for improvements. In order to make it valuable for you, I define your way of configuration. This talk will represent use cases of ReportPortal and the most beneficial configurations of usage. Based on real project examples, with collected metrics and efficiency improvement for them
Prompt Engineering - an Art, a Science, or your next Job Title?Maxim Salnikov
It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions.
This session is based on my research and experiments in Prompt Engineering and is 100% relevant for cloud developers who investigate adding some LLM-powered features to their solutions. It's a guide to building proper prompts for AI to get desired results fast and cost-efficient.
Conversational Artificial Intelligence with Ben Tomlinson and Wayne ThompsonDatabricks
Communicating information to each other is at the heart of the human experience. Data, and the analysis of it, often drives this communication in a business setting. This session aims to give you an understanding of how advances in Artificial Intelligence, specifically Natural Language Interaction (NLI), Natural Language Generation coupled with Deep Learning can create new and exciting opportunities for building analytical based chatbots.
We will talk about how to design and train an NLI system that map requests to deep learning pipelines to derive insights. You will also learn how to apply NLG templates to help facilitate improved understanding and interaction with the chat-bot.
Holland & Barrett: Gen AI Prompt Engineering for Tech teamsDobo Radichkov
Discover Holland & Barrett's Journey into Gen AI: Prompt Engineering and Beyond"
Join us on a captivating journey into the world of Generative AI as Holland & Barrett's Data Team leads a deep dive into the OpenAI ecosystem and the art of prompt engineering. This SlideShare presentation captures the essence of our recent session dedicated to evangelizing the adoption of Gen AI across business and tech within Holland & Barrett. Delve into the nuances of prompt engineering, the comparative analysis of gpt-3.5-turbo and gpt-4, and our recommendations for starting with Prompt Engineering and Retrieval Augmented Generation (RAG). Whether you're a tech enthusiast, a business leader, or an AI aficionado, this presentation offers valuable insights and practical tips to harness the power of AI in your domain.
Looking to change fields and get into tech, but don’t know what skills you need to launch your career? Maximize your marketability by pursuing tech skills in demand for the future!
Chasing Innovation: Exploring the Thrilling World of Prompt Engineering JobsFredReynolds2
Innovation has emerged as the driving force behind technological achievements and societal growth. Prompt engineering jobs, which their dynamic and advanced characteristics can identify, lead the way in this wave of innovation. Prompt engineering has become an important subject supporting productivity, efficiency, and problem-solving across various sectors in the quickly changing world of technology and innovation.
Python, the Language of Science and Engineering for EngineersBoey Pak Cheong
A talk given in November 2016 at IEM Malaysia to engineers, who are new to Python, a broad perspective of what Python is, why it is important to learn it and how it can help in solving/visualization of engineering and scientific tasks and problems.
Presented at All Things Open RTP Meetup
Presented by Brent Laster
Title: Introduction to GitHub Copilot
Abstract: Learn about the future of coding with this presentation, "Introduction to GitHub Copilot." Join author, speaker, and DevOps director Brent Laster as we discuss the GitHub coding assistant that leverages the power of artificial intelligence to augment your development activities. In this session, we'll delve into the core features of GitHub Copilot, explaining what it is, and learning about its ability to generate code snippets, autocompletions, and even entire functions across a variety of programming languages.
Learn how Copilot enhances your coding efficiency by contextualizing suggestions based on your codebase and offering a seamless integration into popular code editors. We'll explore some example applications and look at how developers can harness Copilot to streamline their workflows and boost productivity. Whether you're a seasoned developer or just embarking on your coding journey, this presentation offers a quick introduction to GitHub Copilot's capabilities, as a starting point for you to embrace cutting-edge AI assistance in your coding endeavors.
Youtube Link: https://youtu.be/woVJ4N5nl_s
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka PPT on 'Python Basics' will help you understand what exactly makes Python special and covers all the basics of Python programming along with examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
How do OpenAI GPT Models Work - Misconceptions and Tips for DevelopersIvo Andreev
Have you ever wondered why GPT models work? Do you ask questions like:
◉ How does GPT work? Why does the same problem receive different answers for different users? Is there a way to improve explainability? ◉ Can GPT model provide its sources? Why does Bing chat work differently? What are my ways to have better performance and improve completions? ◉ How can I work with data in my enterprise? What practical business cases could a generative AI model fit solving?
If you are tired of sessions just scratching the surface of OpenAI GPT, this one will go deeper and answer questions like why, why not and how.
Key Terms; ChatGPT Enterprise; Top Questions; Enterprise Data; Azure Search; Functions; Embeddings; Context Encoding; General Intelligence; Emerging Abilities; Chain of Thought; Plugins; Multimodal with DALL-E; Project Florence
POWERED BY -- teachersduniya.com
This is the based on python programming language which is the very power full language . you can also learn whole about overview python.
Powerful tools give a lot room for improvements. In order to make it valuable for you, I define your way of configuration. This talk will represent use cases of ReportPortal and the most beneficial configurations of usage. Based on real project examples, with collected metrics and efficiency improvement for them
Prompt Engineering - an Art, a Science, or your next Job Title?Maxim Salnikov
It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions.
This session is based on my research and experiments in Prompt Engineering and is 100% relevant for cloud developers who investigate adding some LLM-powered features to their solutions. It's a guide to building proper prompts for AI to get desired results fast and cost-efficient.
Conversational Artificial Intelligence with Ben Tomlinson and Wayne ThompsonDatabricks
Communicating information to each other is at the heart of the human experience. Data, and the analysis of it, often drives this communication in a business setting. This session aims to give you an understanding of how advances in Artificial Intelligence, specifically Natural Language Interaction (NLI), Natural Language Generation coupled with Deep Learning can create new and exciting opportunities for building analytical based chatbots.
We will talk about how to design and train an NLI system that map requests to deep learning pipelines to derive insights. You will also learn how to apply NLG templates to help facilitate improved understanding and interaction with the chat-bot.
Holland & Barrett: Gen AI Prompt Engineering for Tech teamsDobo Radichkov
Discover Holland & Barrett's Journey into Gen AI: Prompt Engineering and Beyond"
Join us on a captivating journey into the world of Generative AI as Holland & Barrett's Data Team leads a deep dive into the OpenAI ecosystem and the art of prompt engineering. This SlideShare presentation captures the essence of our recent session dedicated to evangelizing the adoption of Gen AI across business and tech within Holland & Barrett. Delve into the nuances of prompt engineering, the comparative analysis of gpt-3.5-turbo and gpt-4, and our recommendations for starting with Prompt Engineering and Retrieval Augmented Generation (RAG). Whether you're a tech enthusiast, a business leader, or an AI aficionado, this presentation offers valuable insights and practical tips to harness the power of AI in your domain.
Looking to change fields and get into tech, but don’t know what skills you need to launch your career? Maximize your marketability by pursuing tech skills in demand for the future!
Chasing Innovation: Exploring the Thrilling World of Prompt Engineering JobsFredReynolds2
Innovation has emerged as the driving force behind technological achievements and societal growth. Prompt engineering jobs, which their dynamic and advanced characteristics can identify, lead the way in this wave of innovation. Prompt engineering has become an important subject supporting productivity, efficiency, and problem-solving across various sectors in the quickly changing world of technology and innovation.
How To Get Started In Tech With No ExperienceUdemezue John
The tech industry in the US has generated more than 200,000 new jobs every year since 2010, with more jobs still being created as we speak.
Tech Jobs globally are surging at an all-time high with more people’s talents in demand than ever, and new roles being created as a result of the overload in the industry. The need for more division of labour has made Tech a juicy role for anyone with or without a degree.
Before now, we had the nerdy people constitute the majority, but now it’s becoming a different case. We now have tech roles for code and no-code individuals.
Is Web Development Dying? Investigating Shifts in the Market and New Avenues
Is Web Development Dying?
5 January 2024
•
msrbuzz.com
In the fast-paced world of technology, debates about the longevity of web development have surfaced. This inquiry delves into the dynamic landscape of digital innovation, examining the factors that contribute to the perception of web development’s potential decline. Join us as we navigate through the transformative trends, emerging technologies, and shifting paradigms, to better understand the prospects of web development in an ever-evolving digital ecosystem. Is Web Development Dying?
Table of Contents
The Debated Assertion
The Surge of No-Code Tools and AI
The Significance of Fundamentals and First Principles Thinking
The Evolving Hiring Scenario
Navigating the Changing Landscape
Is Web Development Dying?
Conclusion
The Debated Assertion
In today’s swiftly evolving tech landscape, there’s a contentious suggestion that web development is obsolete. While this statement might sound extreme, it carries some validity when considering the current market trends and the progress in artificial intelligence (AI). In this blog, we will explore this topic, discussing the evolving dynamics of the industry, especially regarding job opportunities, and suggest measures to stay pertinent in this changing landscape.
The Surge of No-Code Tools and AI
A primary argument supporting the demise of web development is the rise of no-code tools and AI. Creating a website has become simpler than ever, thanks to these tools. Remarkably, AI can now fabricate websites surpassing the quality of those created by most humans in a matter of days. This poses a challenge to web developers who fear their skills might become obsolete.
AI is consistently advancing, and we can anticipate even more sophisticated AI systems in the coming months. These systems will comprehend website descriptions and generate code accordingly. This trend is evident in educational settings, where students grasp intricate concepts like the single-threaded and asynchronous nature of JavaScript more swiftly than before.
Moreover, tech awareness is growing in countries like India. Engineering degrees no longer guarantee jobs, and more individuals are venturing into coding. With AI as a new competitor, it’s vital to possess strong fundamentals to stand out.
The Significance of Fundamentals and First Principles Thinking
While anyone can code, a robust foundation in fundamentals distinguishes developers. In the face of AI-powered coding, those with solid fundamentals can effectively use AI as a tool instead of relying on it entirely.
A personal anecdote shared by the author underscores the importance of fundamentals. Delegating a task to a colleague who used AI to write code resulted in a production breakdown due to a lack of understanding of certain fundamentals. The author, armed with strong fundamentals, quickly identified and rectified the issue.
Want to make a career in artificial intelligence? AI Artificial Intelligence, One of the most important terms that you should know if you really want to be the part of future and competition. AI has the potential and power to increase your productivity, it can help you to create new jobs, and also it can help you to raise your living Standards. So, if you are interested in making a career in artificial intelligence as an AI engineer, then you come to the right place because, in this blog, I will discuss all the important aspects of a career in AI (artificial intelligence). So, let’s get started!!
Computer Courses in Delhi.: Learners must be careful while selecting a career. With limited career possibilities, unemployment and contest among young individuals have grown.
Information Technology (IT) is a tremendously increasing sector that has achieved momentum over the previous few years. In today’s time planet, it is a must to have some understanding of computer-related information.
Top Strategic Technology Trends for 2022.docxAdvance Tech
Strategic technology trends can be difficult to predict, but many technology observers think that the 2024 Olympics and the autonomous cars of the 2020s are inevitable.
Regardless, strategic technology trends can inspire companies and entrepreneurs looking to maximize their exposure and reach, increase their revenue, and drive new products and services.
Several technologies will be an important part of the 2022 strategy of any large firm, including software, cloud computing, and mobile technology.
A discussion that the following is the main focus on the impact of these technologies on the business functions of large firms and their future strategies. We finally brought together the sectors that will largely influence the future world economy in this post.
Strategic technology is the combination of strategic thinking, best practices, and financial effectiveness.
https://advancetech.info/gartners-top-10-strategic-technology-trends-for-2022/
What skills are essential for a successful career in information technology.docxSurendra Gusain
If you want to have a successful career in the information technology sector, you must have some essential skills to have a successful career in the information technological field. The information technology industry is a good career choice for those individuals who are looking for a long-term career and dynamic growth. To get succeeded in the IT industry, you need to have a perfect combination of hard and soft skills. If you have a good grip on important IT skills, you can easily stand over the other candidates. In today’s blog, we will be discussing ‘What skills are essential for a successful career in information technology?’ So, without wasting any time let’s get started!!!
How machine learning will affect software development venkatvajradhar1
Modern software systems release “machine data” (logs, metrics, etc.) that are critical to detecting and understanding the abuse, but the size and complexity of this data far exceeds the human ability to perform the necessary analysis and capture.
AI (Artificial Intelligence) technology is now poised to
transform every industry, just as electricity did 100 years
ago. Between now and 2030, it will create an estimated
$13 trillion of GDP growth. While it has already created
tremendous value in leading technology companies
such as Google, Baidu, Microsoft and Facebook, much
of the additional waves of value creation will go beyond
the software sector.
This AI Transformation Playbook draws on insights
gleaned from leading the Google Brain team and
the Baidu AI Group, which played leading roles in
transforming both Google and Baidu into great AI
companies. It is possible for any enterprise to follow
this Playbook and become a strong AI company,
though these recommendations are tailored primarily
for larger enterprises with a market cap/valuation from
$500M to $500B.
PLAYBOOK STEPS
1. Execute pilot projects to gain momentum
2. Build an in-house AI team
3. Provide broad AI training
4. Develop an AI strategy
5. Develop internal and external communications
In today's tech-driven world, the integration of artificial intelligence (AI) into applications has become increasingly prevalent. From personalized recommendations to intelligent chatbots, AI enhances user experiences and optimizes processes. However, building an AI app can seem daunting to those unfamiliar with the process. Fear not! This guide aims to demystify the journey, offering step-by-step insights into how to build an AI app from scratch.
The impact of computer coding on modern life is so universal that many of us don't think twice about it! But behind every social media post, streaming service selection, and smartphone app is, of course, code: a system of functions, commands, and rules written in a particular language to make our digital technologies function smoothly. Even with the rise of artificial intelligence and its increasing power to help write and edit computer code, human coders are still behind the scenes.
For many students, learning basic coding can be an empowering experience that teaches a wide range of skills: creativity, problem-solving, perseverance, confidence, and more. Teaching coding to your students helps them to better understand how the modern world works, and perhaps, someday, shape the digital future.
* structured according to
3 phases of go-to-market processes
* first slide: report the north star KPI for that phase
* next slides provide quantitative and qualitative detail on that KPI result
* final slides verify that the KPIs from prior phases are still healthy
* LIR (Leading Indicator of Retention) tracked in all phases
Slide template: https://docs.google.com/presentation/d/1Po17BL-sF_LUWoKicLo1bwHXqOsFV8kzBxCLXLxVvKs/edit?pli=1#slide=id.g2830b63f306_0_268
* Boston Global Forum established AI World Society 2017
* Seven-Layer model of world
* Reflects a society deeply rooted in applied AI
* Vision: ushering in an Age of Global Enlightenment
* Building AIWS Economy-Politics in the AI Age
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
4 questions for founders considering using *Generative AI tech in their startup design:
1. Which layer in your tech stack?
2. Market preferences?
3. Is AI your business moat?
4. AI depth?
*(Tomas Tunguz explores AI capabilities by asking it to create this presentation)
* "Undiscussables" are collective, unconscious behaviors
*Difficult to uncover
*Aware of isolated problems
*Cannot connect the dots
*Wrong conclusions about team inefficiencies & poor performance
The ultimate B2B list:
100 startups knocking on the door of the unicorn paddock
Additional reports: https://sifted.eu/intelligence/reports/europe-s-rising-100
Is crypto eating fintech?
Digital assets are fighting for their place alongside stocks, shares and other asset classes.
Digital assets are changing the ecosystem & infrastructure that has been built around buying,
trading and holding those assets.
Metaverse is moving towards becoming the next iteration of
the internet, or Web3.
Users should increasingly be able to access a host of use cases, including
commerce, art, media, advertising, healthcare, and social collaboration.
Attention Economics: B2b Startup Case StudyCMassociates
*Sales activities + performance metrics linked to KPIs
*Investor expectations measured against peer group companies
* Are your CAC to LTV ratios attractive?
*What levers do you have to achieve those ratios?
* Sustainable pre-revenue value creation focused on key account development
Catherine McQuaid, a Toronto-based business-development consultant (#B2BsalesYouTube), has a theory. She calls it “Big-Game Hunters in the Urban Jungle.”
I think a better name is “Off-Target Marketing.” Either way, it’s an approach to prospecting that might help you build stronger relationships with hard-to-reach executives.
Insights into the process of successful cross-sector partnering
* create better case study collection and dissemination methods
* deepen understanding of case studies as tools for change.
Unveiling the Secrets How Does Generative AI Work.pdfSam H
At its core, generative artificial intelligence relies on the concept of generative models, which serve as engines that churn out entirely new data resembling their training data. It is like a sculptor who has studied so many forms found in nature and then uses this knowledge to create sculptures from his imagination that have never been seen before anywhere else. If taken to cyberspace, gans work almost the same way.
Affordable Stationery Printing Services in Jaipur | Navpack n PrintNavpack & Print
Looking for professional printing services in Jaipur? Navpack n Print offers high-quality and affordable stationery printing for all your business needs. Stand out with custom stationery designs and fast turnaround times. Contact us today for a quote!
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...BBPMedia1
Grote partijen zijn al een tijdje onderweg met retail media. Ondertussen worden in dit domein ook de kansen zichtbaar voor andere spelers in de markt. Maar met die kansen ontstaan ook vragen: Zelf retail media worden of erop adverteren? In welke fase van de funnel past het en hoe integreer je het in een mediaplan? Wat is nu precies het verschil met marketplaces en Programmatic ads? In dit half uur beslechten we de dilemma's en krijg je antwoorden op wanneer het voor jou tijd is om de volgende stap te zetten.
Personal Brand Statement:
As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
VAT Registration Outlined In UAE: Benefits and Requirementsuae taxgpt
Vat Registration is a legal obligation for businesses meeting the threshold requirement, helping companies avoid fines and ramifications. Contact now!
https://viralsocialtrends.com/vat-registration-outlined-in-uae/
Digital Transformation and IT Strategy Toolkit and TemplatesAurelien Domont, MBA
This Digital Transformation and IT Strategy Toolkit was created by ex-McKinsey, Deloitte and BCG Management Consultants, after more than 5,000 hours of work. It is considered the world's best & most comprehensive Digital Transformation and IT Strategy Toolkit. It includes all the Frameworks, Best Practices & Templates required to successfully undertake the Digital Transformation of your organization and define a robust IT Strategy.
Editable Toolkit to help you reuse our content: 700 Powerpoint slides | 35 Excel sheets | 84 minutes of Video training
This PowerPoint presentation is only a small preview of our Toolkits. For more details, visit www.domontconsulting.com
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
𝐓𝐉 𝐂𝐨𝐦𝐬 provides unlimited package services including such as Event organizing, Event planning, Event production, Manpower, PR marketing, Design 2D/3D, VIP protocols, Interpreter agency, etc.
Sports events - Golf competitions/billiards competitions/company sports events: dynamic and challenging
⭐ 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬:
➢ 2024 BAEKHYUN [Lonsdaleite] IN HO CHI MINH
➢ SUPER JUNIOR-L.S.S. THE SHOW : Th3ee Guys in HO CHI MINH
➢FreenBecky 1st Fan Meeting in Vietnam
➢CHILDREN ART EXHIBITION 2024: BEYOND BARRIERS
➢ WOW K-Music Festival 2023
➢ Winner [CROSS] Tour in HCM
➢ Super Show 9 in HCM with Super Junior
➢ HCMC - Gyeongsangbuk-do Culture and Tourism Festival
➢ Korean Vietnam Partnership - Fair with LG
➢ Korean President visits Samsung Electronics R&D Center
➢ Vietnam Food Expo with Lotte Wellfood
"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
What are the main advantages of using HR recruiter services.pdfHumanResourceDimensi1
HR recruiter services offer top talents to companies according to their specific needs. They handle all recruitment tasks from job posting to onboarding and help companies concentrate on their business growth. With their expertise and years of experience, they streamline the hiring process and save time and resources for the company.
2. PAGE 2
"AI is the new
electricity. It will
transform and improve
all areas of human life."
Andrew Ng
3. PAGE 3
Table of
Contents
Introduction: Coding AI is the New Literacy.
Chapter 1: Three Steps to Career Growth.
Chapter 2: Learning Technical Skills for a
Promising AI Career.
Chapter 3: Should You Learn Math to Get a Job
in AI?
Chapter 4: Scoping Successful AI Projects.
Chapter 5: Finding Projects that Complement
Your Career Goals.
Chapter 6: Building a Portfolio of Projects that
Shows Skill Progression.
Chapter 7: A Simple Framework for Starting Your AI
Job Search.
Chapter 8: Using Informational Interviews to Find
the Right Job.
Chapter 9: Finding the Right AI Job for You.
Chapter 10: Keys to Building a Career in AI.
Chapter 11: Overcoming Imposter Syndrome.
Final Thoughts: Make Every Day Count.
LEARNING
PROJECTS
JOB
4. PAGE 4
Coding AI Is the New Literacy
Today we take it for granted that many people know how to read and write. Someday, I hope,
it will be just as common that people know how to write code, specifically for AI.
Several hundred years ago, society didn’t view language literacy as a necessary skill. A small
number of people learned to read and write, and everyone else let them do the reading and
writing. It took centuries for literacy to spread, and now society is far richer for it.
Words enable deep human-to-human communication. Code is the deepest form of human-to-
machine communication. As machines become more central to daily life, that communication
becomes ever more important.
Traditional software engineering — writing programs that explicitly tell a computer sequences
of steps to execute — has been the main path to code literacy. Many introductory programming
classes use creating a video game or building a website as examples. But AI, machine learning,
and data science offer a new paradigm in which computers extract knowledge from data. This
technology offers an even better pathway to coding.
Many Sundays, I buy a slice of pizza from my neighborhood pizza parlor. The gentleman
behind the counter has little reason to learn how to build a video game or write his own
website software (beyond personal growth and the pleasure of gaining a new skill).
But AI and data science have great value even for a pizza maker. A linear regression model might
enable him to better estimate demand so he can optimize the restaurant’s staffing and supply
chain. He could better predict sales of Hawaiian pizza — my favorite! — so he could make more
Hawaiian pies in advance and reduce the amount of time customers had to wait for them.
Uses of AI and data science can be found in almost any situation that produces data. Thus,
a wide variety of professions will find more uses for custom AI applications and data-derived
insights than for traditional software engineering. This makes literacy in AI-oriented coding
even more valuable than traditional coding. It could enable countless individuals to harness
data to make their lives richer.
I hope the promise of building basic AI applications, even more than that of building basic
traditional software, encourages more people to learn how to code. If society embraces this
new form of literacy as it has the ability to read and write, we will all benefit.
Introduction
6. PAGE 6
The rapid rise of AI has led to a rapid rise in AI jobs, and many people are building exciting
careers in this field. A career is a decades-long journey, and the path is not straightforward.
Over many years, I’ve been privileged to see thousands of students, as well as engineers in
companies large and small, navigate careers in AI.
Here’s a framework for charting your own course.
Later, you will work
on finding a job.
Throughout this
process, you’ll continue
to learn and work on
meaningful projects.
Chapters with the
focus on a job search.
Three key steps of career growth are learning foundational skills, working on projects (to
deepen your skills, build a portfolio, and create impact), and finding a job. These steps stack
on top of each other:
Three Steps to Career Growth
LEARNING PROJECTS JOB
CHAPTER 1
Initially, you
focus on learning
foundational skills.
Chapters with the
cover topics about
learning foundational
technical skills.
After having gained
foundational technical
skills, you will begin
working on projects.
During this period, you’ll
also keep learning.
Chapters with the
focus on projects.
7. PAGE 7
These phases apply in a wide
range of professions, but AI
involves unique elements.
For example:
Three Steps to Career Growth
AI is nascent, and many technologies are still evolving. While the
foundations of machine learning and deep learning are maturing —
and coursework is an efficient way to master them — beyond these
foundations, keeping up-to-date with changing technology is more
important in AI than fields that are more mature.
Learning foundational skills is a career-long process:
Thiscanmakeitchallengingtofindasuitableproject,estimatetheproject’s
timeline and return on investment, and set expectations. In addition, the
highly iterative nature of AI projects leads to special challenges in project
management: How can you come up with a plan for building a system
when you don’t know in advance how long it will take to achieve the target
accuracy? Even after the system has hit the target, further iteration may
be necessary to address post-deployment drift.
Working on projects often means collaborating with
stakeholders who lack expertise in AI:
While searching for a job in AI can be similar to searching for a job in
other sectors, there are also important differences. Many companies are
still trying to figure out which AI skills they need, and how to hire people
who have them. Things you’ve worked on may be significantly different
than anything your interviewer has seen, and you’re more likely to have to
educate potential employers about some elements of your work.
Inconsistent opinions on AI skills and jobs roles:
CHAPTER 1
As you go through each step, you should also build a supportive community. Having friends and
allies who can help you — and who you strive to help — makes the path easier. This is true whether
you’re taking your first steps or you’ve been on the journey for years.
LEARNING
PROJECTS
JOB
9. PAGE 9
In the previous chapter, I introduced three key steps for building a career in AI: learning
foundational technical skills, working on projects, and finding a job, all of which is supported
by being part of a community. In this chapter, I’d like to dive more deeply into the first step:
learning foundational skills.
More research papers have been published on AI than anyone can read in a lifetime. So, when
learning, it’s critical to prioritize topic selection. I believe the most important topics for a technical
career in machine learning are:
Foundational machine learning skills: For example, it’s important to understand models such
as linear regression, logistic regression, neural networks, decision trees, clustering, and anomaly
detection. Beyond specific models, it’s even more important to understand the core concepts
behind how and why machine learning works, such as bias/variance, cost functions, regularization,
optimization algorithms, and error analysis.
Deep learning: This has become such a large fraction of machine learning that it’s hard to excel
in the field without some understanding of it! It’s valuable to know the basics of neural networks,
practical skills for making them work (such as hyperparameter tuning), convolutional networks,
sequence models, and transformers.
Softwaredevelopment: While you can get a job and make huge contributions with only machine
learning modeling skills, your job opportunities will increase if you can also write good software
to implement complex AI systems. These skills include programming fundamentals, data
structures (especially those that relate to machine learning, such as data frames), algorithms
(including those related to databases and data manipulation), software design, familiarity with
Python, and familiarity with key libraries such as TensorFlow or PyTorch, and scikit-learn.
Mathrelevanttomachinelearning: Key areas include linear algebra (vectors, matrices, and various
manipulations of them) as well as probability and statistics (including discrete and continuous
probability, standard probability distributions, basic rules such as independence and Bayes’ rule,
and hypothesis testing). In addition, exploratory data analysis (EDA) — using visualizations and other
methods to systematically explore a dataset — is an underrated skill. I’ve found EDA particularly
useful in data-centric AI development, where analyzing errors and gaining insights can really help
drive progress! Finally, a basic intuitive understanding of calculus will also help. The math needed
to do machine learning well has been changing. For instance, although some tasks require calculus,
improved automatic differentiation software makes it possible to invent and implement new neural
network architectures without doing any calculus. This was almost impossible a decade ago.
Learning Technical Skills For a Promising AI Career CHAPTER 2
10. PAGE 10
This is a lot to learn!
Even after you master everything on this list, I hope you’ll keep learning and continue to deepen
your technical knowledge. I’ve known many machine learning engineers who benefitted from
deeper skills in an application area such as natural language processing or computer vision, or in
a technology area such as probabilistic graphical models or building scalable software systems.
How do you gain these skills? There’s a lot of good content on the internet, and in theory,
reading dozens of web pages could work. But when the goal is deep understanding, reading
disjointed web pages is inefficient because they tend to repeat each other, use inconsistent
terminology (which slows you down), vary in quality, and leave gaps. That’s why a good course
— in which a body of material has been organized into a coherent and logical form — is often the
most time-efficient way to master a meaningful body of knowledge. When you’ve absorbed the
knowledge available in courses, you can switch over to research papers and other resources.
Finally, no one can cram everything they need to know over a weekend or even a month. Everyone I
know who’s great at machine learning is a lifelong learner. Given how quickly our field is changing,
there’s little choice but to keep learning if you want to keep up.
How can you maintain a steady pace of learning for years? If you can cultivate the habit of
learning a little bit every week, you can make significant progress with what feels like less effort.
Learning Technical Skills For a Promising AI Career CHAPTER 2
11. PAGE 11
The Best Way to Build
a New Habit
One of my favorite books is BJ Fogg’s, Tiny Habits: The Small Changes That Change
Everything. Fogg explains that the best way to build a new habit is to start small
and succeed, rather than start too big and fail. For example, rather than trying to
exercise for 30 minutes a day, he recommends aspiring to do just one push-up, and
doing it consistently.
This approach may be helpful to those of you who want to spend more time studying.
If you start by holding yourself accountable for watching, say, 10 seconds of an
educational video every day — and you do so consistently — the habit of studying daily
will grow naturally. Even if you learn nothing in that 10 seconds, you’re establishing the
habit of studying a little every day. On some days, maybe you’ll end up studying for an
hour or longer.
13. PAGE 13
Should you Learn Math to Get a Job in AI? CHAPTER 3
Is math a foundational skill for AI? It’s always nice to know more math! But there’s so much to
learn that, realistically, it’s necessary to prioritize. Here’s how you might go about strengthening
your math background.
To figure out what’s important to know, I find it useful to ask what you need to know to make
the decisions required for the work you want to do. At DeepLearning.AI, we frequently ask,
“What does someone need to know to accomplish their goals?” The goal might be building a
machine learning model, architecting a system, or passing a job interview.
Understanding the math behind algorithms you use is often helpful, since it enables you to
debug them. But the depth of knowledge that’s useful changes over time. As machine learning
techniques mature and become more reliable and turnkey, they require less debugging, and a
shallower understanding of the math involved may be sufficient to make them work.
For instance, in an earlier era of machine learning, linear algebra libraries for solving linear
systems of equations (for linear regression) were immature. I had to understand how these
libraries worked so I could choose among different libraries and avoid numerical roundoff
pitfalls. But this became less important as numerical linear algebra libraries matured.
Deep learning is still an emerging technology, so when you train a neural network and the
optimization algorithm struggles to converge, understanding the math behind gradient
descent, momentum, and the Adam optimization algorithm will help you make better decisions.
Similarly, if your neural network does something funny — say, it makes bad predictions on
images of a certain resolution, but not others — understanding the math behind neural network
architectures puts you in a better position to figure out what to do.
Of course, I also encourage learning driven by curiosity. If something interests you, go ahead
and learn it regardless of how useful it might turn out to be! Maybe this will lead to a creative
spark or technical breakthrough.
How much math do you need to know to be a machine learning engineer?
15. PAGE 15
One of the most important skills of an AI architect is the ability to identify ideas that are worth
working on. These next few chapters will discuss finding and working on projects so you can gain
experience and build your portfolio.
Over the years, I’ve had fun applying machine learning to manufacturing, healthcare, climate
change, agriculture, ecommerce, advertising, and other industries. How can someone who’s not
an expert in all these sectors find meaningful projects within them? Here are five steps to help
you scope projects.
Identify a business problem (not an AI problem). I like to find
a domain expert and ask, “What are the top three things
that you wish worked better? Why aren’t they working yet?”
For example, if you want to apply AI to climate change, you
might discover that power-grid operators can’t accurately
predict how much power intermittent sources like wind
and solar might generate in the future.
Brainstorm AI solutions. When I was younger, I used to
execute on the first idea I was excited about. Sometimes
this worked out okay, but sometimes I ended up missing
an even better idea that might not have taken any more
effort to build. Once you understand a problem, you can
brainstorm potential solutions more efficiently. For instance,
to predict power generation from intermittent sources, we
might consider using satellite imagery to map the locations
of wind turbines more accurately, using satellite imagery
to estimate the height and generation capacity of wind
turbines, or using weather data to better predict cloud cover
and thus solar irradiance. Sometimes there isn’t a good AI
solution, and that’s okay too.
Scoping Successful AI Projects CHAPTER 4
Step 1
Step 2
16. PAGE 16
Determine milestones. Once you’ve deemed a project sufficiently
valuable, the next step is to determine the metrics to aim for. This
includes both machine learning metrics (such as accuracy) and
business metrics (such as revenue). Machine learning teams are often
most comfortable with metrics that a learning algorithm can optimize.
But we may need to stretch outside our comfort zone to come up
with business metrics, such as those related to user engagement,
revenue, and so on. Unfortunately, not every business problem can be
reduced to optimizing test set accuracy! If you aren’t able to determine
reasonable milestones, it may be a sign that you need to learn more
about the problem. A quick proof of concept can help supply the
missing perspective.
Assess the feasibility and value of potential solutions. You can determine
whether an approach is technically feasible by looking at published work,
what competitors have done, or perhaps building a quick proof of concept
implementation. You can determine its value by consulting with domain
experts (say, power-grid operators, who can advise on the utility of the
potential solutions mentioned above).
Budget for resources. Think through everything you’ll need to get the
project done including data, personnel, time, and any integrations or
support you may need from other teams. For example, if you need funds
to purchase satellite imagery, make sure that’s in the budget.
Working on projects is an iterative process. If, at any step, you find that the current direction is
infeasible, return to an earlier step and proceed with your new understanding. Is there a domain
that excites you where AI might make a difference? I hope these steps will guide you in exploring it
through project work — even if you don’t yet have deep expertise in that field. AI won’t solve every
problem, but as a community, let’s look for ways to make a positive impact wherever we can.
Scoping Successful AI Projects CHAPTER 4
Step 3
Step 4
Step 5
18. PAGE 18
It goes without saying that we should only work on projects that are responsible, ethical, and
beneficial to people. But those limits leave a large variety to choose from. In the previous chapter,
I wrote about how to identify and scope AI projects. This chapter and the next have a slightly
different emphasis: picking and executing projects with an eye toward career development.
A fruitful career will include many projects, hopefully growing in scope, complexity, and impact
over time. Thus, it is fine to start small. Use early projects to learn and gradually step up to
bigger projects as your skills grow.
When you’re starting out, don’t expect others to hand great ideas or resources to you on a platter.
Many people start by working on small projects in their spare time. With initial successes — even
small ones — under your belt, your growing skills increase your ability to come up with better
ideas, and it becomes easier to persuade others to help you step up to bigger projects.
Finding Projects that Compliment Your Career Goals CHAPTER 5
Join existing projects. If you find someone else with an idea, ask to join their project.
Keep reading and talking to people. I come up with new ideas whenever I spend a lot of
time reading, taking courses, or talking with domain experts. I’m confident that you will, too.
Focus on an application area. Many researchers are trying to advance basic AI technology
— say, by inventing the next generation of transformers or further scaling up language
models — so, while this is an exciting direction, it is also very hard. But the variety of
applications to which machine learning has not yet been applied is vast! I’m fortunate to
have been able to apply neural networks to everything from autonomous helicopter flight to
online advertising, partly because I jumped in when relatively few people were working on
those applications. If your company or school cares about a particular application, explore
the possibilities for machine learning. That can give you a first look at a potentially creative
application — one where you can do unique work — that no one else has done yet.
✓
✓
✓
What if you don’t have any project ideas?
Here are a few ways to generate them:
19. PAGE 19
Develop a side hustle. Even if you have a full-time job, a fun project that may or may not
develop into something bigger can stir the creative juices and strengthen bonds with
collaborators. When I was a full-time professor, working on online education wasn’t part of
my “job” (which was doing research and teaching classes). It was a fun hobby that I often
worked on out of passion for education. My early experiences in recording videos at home
helped me later in working on online education in a more substantive way. Silicon Valley
abounds with stories of startups that started as side projects. As long as it doesn’t create a
conflict with your employer, these projects can be a stepping stone to something significant.
Will the project help you grow technically? Ideally, it should be challenging enough to
stretch your skills but not so hard that you have little chance of success. This will put you
on a path toward mastering ever-greater technical complexity.
Do you have good teammates to work with? If not, are there people you can discuss things
with? We learn a lot from the people around us, and good collaborators will have a huge
impact on your growth.
Can it be a stepping stone? If the project is successful, will its technical complexity and/
or business impact make it a meaningful stepping stone to larger projects? If the project
is bigger than those you’ve worked on before, there’s a good chance it could be such a
stepping stone.
✓
✓
✓
✓
Given a few project ideas, which one should you jump into?
Here’s a quick checklist of factors to consider:
Finally, avoid analysis paralysis. It doesn’t make sense to spend a month deciding whether to
work on a project that would take a week to complete. You'll work on multiple projects over
the course of your career, so you’ll have ample opportunity to refine your thinking on what’s
worthwhile. Given the huge number of possible AI projects, rather than the conventional “ready,
aim, fire” approach, you can accelerate your progress with “ready, fire, aim.”
Finding Projects that Compliment Your Career Goals CHAPTER 5
20. PAGE 20
Working on projects requires making tough choices about what to build and how to go
about it. Here are two distinct styles:
Sayyou’vebuiltacustomer-servicechatbotforretailers,andyouthinkitcouldhelprestaurants,
too. Should you take time to study the restaurant market before starting development, moving
slowly but cutting the risk of wasting time and resources? Or jump in right away, moving
quickly and accepting a higher risk of pivoting or failing?
Both approaches have their advocates, and the best choice depends on the situation.
Ready, Aim, Fire tends to be superior when the cost of execution is high and a study can shed
light on how useful or valuable a project could be. For example, if you can brainstorm a few
other use cases (restaurants, airlines, telcos, and so on) and evaluate these cases to identify
the most promising one, it may be worth taking the extra time before committing to a direction.
Ready, Fire, Aim tends to be better if you can execute at low cost and, in doing so, determine
whether the direction is feasible and discover tweaks that will make it work. For example, if
you can build a prototype quickly to figure out if users want the product, and if canceling or
pivoting after a small amount of work is acceptable, then it makes sense to consider jumping
in quickly. When taking a shot is inexpensive, it also makes sense to take many shots. In this
case, the process is actually Ready, Fire, Aim, Fire, Aim, Fire, Aim, Fire.
After agreeing upon a project direction, when it comes to building a machine learning model
that’s part of the product, I have a bias toward Ready, Fire, Aim. Building models is an iterative
process. For many applications, the cost of training and conducting error analysis is not
prohibitive. Furthermore, it is very difficult to carry out a study that will shed light on the
appropriate model, data, and hyperparameters. So it makes sense to build an end-to-end
system quickly and revise it until it works well.
But when committing to a direction means making a costly investment or entering a one-
way door (meaning a decision that’s hard to reverse), it’s often worth spending more time in
advance to make sure it really is a good idea.
Ready, Fire, Aim
Ready, Aim, Fire: Plan carefully and carry out careful validation. Commit and
execute only when you have a high degree of confidence in a direction.
Ready, Fire, Aim: Jump into development and start executing. This allows you to
discover problems quickly and pivot along the way if necessary.
✓
✓
21. PAGE 21
Building a Portfolio of
Projects that Shows
Skill Progression
CHAPTER 6
PROJECTS
22. PAGE 22
Over the course of a career, you’re likely to work on projects in succession, each growing in
scope and complexity. For example:
The first few projects might be narrowly scoped
homework assignments with predetermined right
answers. These are often great learning experiences!
Eventually, you will gain enough skill to build projects in
which others see more tangible value. This opens the door
to more resources. For example, rather than developing
machine learning systems in your spare time, it might
become part of your job, and you might gain access to more
equipment, compute time, labeling budget, or head count.
You might go on to work on small-scale projects either alone or with friends.
For instance, you might re-implement a known algorithm, apply machine
learning to a hobby (such as predicting whether your favorite sports team
will win), or build a small but useful system at work in your spare time (such
as a machine learning-based script that helps a colleague automate some of
their work). Participating in competitions such as those organized by Kaggle
is also one way to gain experience.
Successes build on each other, opening the door to
more technical growth, more resources, and increasingly
significant project opportunities.
1. Class projects:
3. Creating value
2. Personal projects
4. Rising scope and complexity
Building a Portfolio of Projects That Shows Skill Progression CHAPTER 6
23. PAGE 23
Each project is only one step on a longer journey, hopefully one that has a positive impact. In addition:
Don’t worry about starting too small. One of my first machine learning research projects involved
training a neural network to see how well it could mimic the sin(x) function. It wasn’t very useful, but
was a great learning experience that enabled me to move on to bigger projects.
Building a portfolio of projects, especially one
that shows progress over time from simple to
complex undertakings, will be a big help when
it comes to looking for a job.
Communication is key. You need to be able to explain your thinking if you want others to see
the value in your work and trust you with resources that you can invest in larger projects. To get
a project started, communicating the value of what you hope to build will help bring colleagues,
mentors, and managers onboard — and help them point out flaws in your reasoning. After you’ve
finished, the ability to explain clearly what you accomplished will help convince others to open the
door to larger projects.
Leadership isn’t just for managers. When you reach the point of working on larger AI projects that
require teamwork, your ability to lead projects will become more important, whether or not you are
in a formal position of leadership. Many of my friends have successfully pursued a technical rather
than managerial career, and their ability to help steer a project by applying deep technical insights
— for example, when to invest in a new technical architecture or collect more data of a certain type
— allowed them to grow as leaders and also helped significantly improve the project.
Building a Portfolio of Projects That Shows Skill Progression CHAPTER 6
24. PAGE 24
A Simple Framework
for Starting Your AI
Job Search
CHAPTER 7
JOBS
25. PAGE 25
Finding a job has a few predictable steps that include selecting the companies to which you
want to apply, preparing for interviews, and finally picking a role and negotiating a salary and
benefits. In this chapter, I’d like to focus on a framework that’s useful for many job seekers in
AI, especially those who are entering AI from a different field.
A product manager at a tech startup who becomes a data scientist at the same company (or a
different one) has switched roles. A marketer at a manufacturing firm who becomes a marketer
in a tech company has switched industries. An analyst in a financial services company who
becomes a machine learning engineer in a tech company has switched both roles and industries.
If you’re looking for your first job in AI, you’ll probably find switching either roles or industries
easier than doing both at the same time. Let’s say you’re the analyst working in financial services:
If you’re considering your next job, ask yourself:
Job
Search
Are you switching roles? For example, if you’re a software engineer,
university student, or physicist who’s looking to become a machine learning
engineer, that’s a role switch.
Are you switching industries? For example, if you work for a healthcare
company, financial services company, or a government agency and want
to work for a software company, that’s a switch in industries.
✓
✓
If you find a data science or machine learning job in financial services, you can continue
to use your domain-specific knowledge while gaining knowledge and expertise in AI. After
working in this role for a while, you’ll be better positioned to switch to a tech company (if
that’s still your goal).
Alternatively, if you become an analyst in a tech company, you can continue to use your
skills as an analyst but apply them to a different industry. Being part of a tech company also
makes it much easier to learn from colleagues about practical challenges of AI, key skills to
be successful in AI, and so on.
✓
✓
A Simple Framework for Starting You AI Job Search CHAPTER 7
TECH
FINANCIAL
SERVICES Role & Industry Switch
Role Switch
Industry
Switch
ANALYST MACHINE LEARNING
ENGINEER
26. PAGE 26
If you’re considering a role switch, a startup can be an easier place to do it than a big company.
While there are exceptions, startups usually don’t have enough people to do all the desired
work. If you’re able to help with AI tasks — even if it’s not your official job — your work is likely
to be appreciated. This lays the groundwork for a possible role switch without needing to leave
the company. In contrast, in a big company, a rigid reward system is more likely to reward you
for doing your job well (and your manager for supporting you in doing the job for which you were
hired), but it’s not as likely to reward contributions outside your job’s scope.
After working for a while in your desired role and industry (for example, a machine learning
engineer in a tech company), you’ll have a good sense of the requirements for that role in that
industry at a more senior level. You’ll also have a network within that industry to help you along.
So future job searches — if you choose to stick with the role and industry — likely will be easier.
When changing jobs, you’re taking a step into the unknown, particularly if you’re switching either
roles or industries. One of the most underused tools for becoming more familiar with a new role
and/or industry is the informational interview. I’ll share more about that in the next chapter.
I’m grateful to Salwa Nur Muhammad, CEO of FourthBrain (a DeepLearning.AI affiliate), for providing
some of the ideas presented in this chapter.
27. PAGE 27
There’s a lot we don’t know about the future: When will we cure Alzheimer’s disease? Who will
win the next election? Or, in a business context, how many customers will we have next year?
With so many changes going on in the world, many people are feeling stressed about the
future, especially when it comes to finding a job. I have a practice that helps me regain a sense
of control. Faced with uncertainty, I try to:
For example, during the Covid-19 pandemic back in March 2020, I did this scenario planning
exercise. I imagined quick (three months), medium (one year), and slow (two years) recoveries
from Covid-19 and made plans for managing each case. These plans have helped me prioritize
where I can.
The same method can apply to personal life, too. If you’re not sure you’ll pass an exam, get a
job offer, or be granted a visa — all of which can be stressful — you can write out what you’d
do in each of the likely scenarios. Thinking through the possibilities and following through on
plans can help you navigate the future effectively no matter what it brings.
Bonus: With training in AI and statistics, you can calculate a probability for each scenario.
I’m a fan of the Superforecasting methodology, in which the judgments of many experts are
synthesized into a probability estimate.
Overcoming Uncertainty
Make a list of plausible scenarios,
acknowledging that I don’t know
which will come to pass.
Create a plan of action
for each scenario.
Start executing actions
that seem reasonable.
Review scenarios and plans
periodically as the future
comes into focus.
1 2
3 4
29. PAGE 29
If you’re preparing to switch roles (say, taking a job as a machine learning engineer for the first
time) or industries (say, working in an AI tech company for the first time), there’s a lot about
your target job that you probably don’t know. A technique known as informational interviewing
is a great way to learn.
An informational interview involves finding someone in a company or role you’d like to know
more about and informally interviewing them about their work. Such conversations are separate
from searching for a job. In fact, it’s helpful to interview people who hold positions that align
with your interests well before you’re ready to kick off a job search.
Prepare for informational interviews by researching the interviewee and company in advance,
so you can arrive with thoughtful questions. You might ask:
Informational interviews are particularly relevant to AI. Because the field is evolving, many
companies use job titles in inconsistent ways. In one company, data scientists might be
expected mainly to analyze business data and present conclusions on a slide deck. In
another, they might write and maintain production code. An informational interview can help
you sort out what the AI people in a particular company actually do.
With the rapid expansion of opportunities in AI, many people will be taking on an AI job for
the first time. In this case, an informational interview can be invaluable for learning what
happens and what skills are needed to do the job well. For example, you can learn what
algorithms, deployment processes, and software stacks a particular company uses. You
may be surprised — if you’re not already familiar with the data-centric AI movement — to
learn how much time most machine learning engineers spend iteratively cleaning datasets.
✓
✓
What do you do in a typical week or day?
What are the most important tasks in this role?
What skills are most important for success?
How does your team work together to accomplish its goals?
What is the hiring process?
Considering candidates who stood out in the past, what enabled them to shine?
✓
✓
✓
✓
✓
✓
Using Informational Interviews to Find the Right Job CHAPTER 8
30. PAGE 30
Finding someone to interview isn’t always easy, but many people who are in senior positions today
received help when they were new from those who had entered the field ahead of them, and many
are eager to pay it forward. If you can reach out to someone who’s already in your network —
perhaps a friend who made the transition ahead of you or someone who attended the same school
as you — that’s great! Meetups such as Pie & AI can also help you build your network.
Finally, be polite and professional, and thank the people you’ve interviewed. And when you get
a chance, please pay it forward as well and help someone coming up after you. If you receive
a request for an informational interview from someone in the DeepLearning.AI community,
I hope you’ll lean in to help them take a step up! If you’re interested in learning more about
informational interviews, I recommend this article from the UC Berkeley Career Center.
I’ve mentioned a few times the importance of your network and community. People you’ve
met, beyond providing valuable information, can also play an invaluable role by referring you to
potential employers.
Using Informational Interviews to Find the Right Job CHAPTER 8
32. PAGE 32
In this chapter, I’d like to
discuss some fine points
of finding a job.
The typical job search follows a fairly predictable path.
Although the process may be familiar, every job search is different. Here are some tips to increase
the odds you’ll find a position that supports your thriving career and enables you to keep growing.
Research roles and companies online or by talking to friends.
Optionally, arrange informal informational interviews with people in companies that appeal to you.
Either apply directly or, if you can, get a referral from someone on the inside.
Interview with companies that give you an invitation.
Receive one or more offers and pick one. Or, if you don’t receive an offer, ask for feedback
from the interviewers, human resources staff, online discussion boards, or anyone in your
network who can help you plot your next move.
✓
✓
✓
✓
✓
Pay attention to the fundamentals. A compelling resume, portfolio of technical projects, and
a strong interview performance will unlock doors. Even if you have a referral from someone in a
company, a resume and portfolio will be your first contact with many people who don’t already
know about you. Update your resume and make sure it clearly presents your education and
experience relevant to the role you want. Customize your communications with each company
to explain why you’re a good fit. Before an interview, ask the recruiter what to expect. Take time
to review and practice answers to common interview questions, brush up key skills, and study
technical materials to make sure they are fresh in your mind. Afterward, take notes to help you
remember what was said.
Proceed respectfully and responsibly. Approach interviews and offer negotiations with a win-
win mindset. Outrage spreads faster than reasonableness on social media, so a story about
how an employer underpaid someone gets amplified, whereas stories about how an employer
treated someone fairly do not. The vast majority of employers are ethical and fair, so don’t let
stories about the small fraction of mistreated individuals sway your approach. If you’re leaving
a job, exit gracefully. Give your employer ample notice, give your full effort through your last
hour on the job, transition unfinished business as best you can, and leave in a way that honors
the responsibilities you were entrusted with.
Finding the Right AI Job For You CHAPTER 9
33. PAGE 33
Choose who to work with. It’s tempting to take a position because of the projects you’ll work
on. But the teammates you’ll work with are at least equally important. We’re influenced by
people around us, so your colleagues will make a big difference. For example, if your friends
smoke, the odds increase that you, too, will smoke. I don’t know of a study that shows this,
but I’m pretty sure that if most of your colleagues work hard, learn continuously, and build AI
to benefit all people, you’re likely to do the same. (By the way, some large companies won’t
tell you who your teammates will be until you’ve accepted an offer. In this case, be persistent
and keep pushing to identify and speak with potential teammates. Strict policies may make it
impossible to accommodate you, but in my mind, that increases the risk of accepting the offer,
as it increases the odds you’ll end up with a manager or teammates who aren’t a good fit.)
Get help from your community. Most of us go job hunting only a small number of times in our
careers, so few of us get much practice at doing it well. Collectively, though, people in your
immediate community probably have a lot of experience. Don’t be shy about calling on them.
Friends and associates can provide advice, share inside knowledge, and refer you to others
who may help. I got a lot of help from supportive friends and mentors when I applied for my
first faculty position, and many of the tips they gave me were very helpful.
I know that the job-search process can be intimidating. Instead of viewing it as a great leap,
consider an incremental approach. Start by identifying possible roles and conducting a handful
of informational interviews. If these conversations tell you that you have more learning to do
before you’re ready to apply, that’s great! At least you have a clear path forward. The most
important part of any journey is to take the first step, and that step can be a small one.
Finding the Right AI Job For You CHAPTER 9
35. PAGE 35
Keys to Building a Career in AI CHAPTER 10
The path to career success in AI is more complex than what I can cover in one short eBook.
Hopefully the previous chapters will give you momentum to move forward.
Here are additional things to think about as you plot your path to success:
When we tackle large projects, we succeed better by
working in teams than individually. The ability to collaborate
with, influence, and be influenced by others is critical.
Thus, interpersonal and communication skills really matter.
(I used to be a pretty bad communicator, by the way.)
1. Teamwork:
I hate networking! As an introvert, having to go to a party
to smile and shake as many hands as possible is an activity
that borders on horrific. I’d much rather stay home and read
a book. Nonetheless, I’m fortunate to have found many
genuine friends in AI; people I would gladly go to bat for
and who I count on as well. No person is an island, and
having a strong professional network can help propel you
forward in the moments when you need help or advice. In
lieu of networking, I’ve found it more helpful to think about
building up a community. So instead of trying to build up
my personal network, I focus instead on building up the
communities that I’m part of. This has the side effect of
helping me meet more people and make friends as well.
2. Networking:
36. PAGE 36
Keys to Building a Career in AI CHAPTER 10
Of all the steps in building a career, this
one tends to receive the most attention.
Unfortunately, there is a lot of bad advice
about this on the internet. (For example, many
articles urge taking an adversarial attitude
toward potential employers, which I don’t think
is helpful.) Although it may seem like finding a
job is the ultimate goal, it’s just one small step
in the long journey of a career.
3. Job search
Few people will know whether you spend
your weekends learning, or binge watching
TV — but they will notice the difference over
time. Many successful people develop good
habits in eating, exercise, sleep, personal
relationships, work, learning, and self-care.
Such habits help them move forward while
staying healthy.
4. Personal discipline
I find that people who aim to lift others during
every step of their own journey often achieve
better outcomes for themselves. How can we
help others even as we build an exciting career
for ourselves?
5. Altruism
38. PAGE 38
Before we dive into the final chapter of this book, I’d like to address the serious matter of
newcomers to AI sometimes experiencing imposter syndrome, where someone — regardless
of their success in the field — wonders if they’re a fraud and really belong in the AI community.
I want to make sure this doesn’t discourage you or anyone else from growing in AI.
An estimated 70 percent of people experience some form of imposter syndrome at some point.
Many talented people have spoken publicly about this experience, including former Facebook
COO Sheryl Sandberg, U.S. first lady Michelle Obama, actor Tom Hanks, and Atlassian co-CEO
Mike Cannon-Brookes. It happens in our community even among accomplished people. If you’ve
never experienced this yourself, that’s great! I hope you’ll join me in encouraging and welcoming
everyone who wants to join our community.
AI is technically complex, and it has its fair share of smart and highly capable people. But it is
easy to forget that to become good at anything, the first step is to suck at it. If you’ve succeeded
at sucking at AI — congratulations, you’re on your way!
I once struggled to understand the math behind linear regression. I was mystified when
logistic regression performed strangely on my data, and it took me days to find a bug in my
implementation of a basic neural network. Today, I still find many research papers challenging
to read, and I recently made an obvious mistake while tuning a neural network hyperparameter
(that fortunately a fellow engineer caught and fixed).
So if you, too, find parts of AI challenging, it’s okay. We’ve all been there. I guarantee that everyone
who has published a seminal AI paper struggled with similar technical challenges at some point.
Let me be clear: If you want to be part of the AI
community, then I welcome you with open arms.
If you want to join us, you fully belong with us!
Overcoming Imposter Syndrome
39. PAGE 39
My three-year-old daughter (who can barely count to 12) regularly tries to teach things to my
one-year-old son. No matter how far along you are — if you’re at least as knowledgeable as a
three-year-old — you can encourage and lift up others behind you. Doing so will help you, too,
as others behind you will recognize your expertise and also encourage you to keep developing.
When you invite others to join the AI community, which I hope you will do, it also reduces any
doubts that you are already one of us.
AI is such an important part of our world that I would like everyone who wants to be part of it
to feel at home as a member of our community. Let’s work together to make it happen.
Here are some things that can help.
Do you have supportive mentors or peers? If you don’t yet, attend Pie & AI or other events,
use discussion boards, and work on finding some. If your mentors or manager don’t support
your growth, find ones who do. I’m also working on how to grow a supportive AI community
and hope to make finding and giving support easier for everyone.
No one is an expert at everything. Recognize what you do well. If what you do well is
understand and explain to your friends one-tenth of the articles in The Batch, then you’re
on your way! Let’s work on getting you to understand two-tenths of the articles.
✓
✓
Overcoming Imposter Syndrome
40. PAGE 40
Make Every Day Count
Final Thoughts
Every year on my birthday, I get to thinking about the days behind and those
that may lie ahead.
When I ask friends, many choose a number in the hundreds of
thousands. (Many others can’t resist calculating the answer,
to my annoyance!)
When I was a grad student, I remember plugging my statistics
into a mortality calculator to figure out my life expectancy.
The calculator said I could expect to live a total of 27,649
days. It struck me how small this number is. I printed it in a
large font and pasted it on my office wall as a daily reminder.
That’s all the days we have to spend with loved ones, learn,
build for the future, and help others. Whatever you’re doing
today, is it worth 1/30,000 of your life?
How many days is a
typical human lifespan?
Maybe you’re good at math; I’m sure you’ll be able to answer the following question
via a quick calculation. But let me ask you a question, and please answer from
your gut, without calculating.
20,000 days 100,000 days
1 million days 5 million days