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TensorFlow-сертифікат: як бізнес-лидери можуть швидко закріплювати AI-команду

DeepLearning.AI22 днi тому23 берез. 2026Impact 8/10
AI Аналіз

Цей курс від DeepLearning.AI навчає практичному використанню TensorFlow для побудови моделей машинного навчання. Він спрямований на розробників, які хочуть стати AI-спеціалістами та заповнити ринок дефіциту фахівців. Lawrence Moroni пояснює, як сертифікат відкриває нові можливості у різних галузях.

Ключові тези

  • TensorFlow — це основний фреймворк для реалізації алгоритмів глибинного навчання та машинного навчання.
  • Професійний сертифікат надає змогу перетворити розробників на AI-спеціалістів.
  • Є значний розрив: приблизно 300 тис. AI-фахівців проти 25 млн розробників.
  • Лоранс Мороні розповідає про свій шлях від раннього AI (Prolog, Lisp) до сучасного глибинного навчання.
  • Курс охоплює основи, такі як fitting X-to-Y залежності та лінійна регресія.
Можливості

🟢 Можливості — компанії можуть швидко підвищити кваліфікацію своїх команд, записуючи розробників на цей сертифікат, скорочуючи час виведення AI-продуктів на ринок. 🔴 Загрози — залежність від одного фреймворку може обмежити гнучкість, якщо технологічний стек зміниться; також перенасичення ринку сертифікатами може зменшити їхню цінність. Для бізнесу важливо поєднувати сертифікацію з практичними проєктами та постійним оновленням навичок.

Нюанси

Хоча курс позиціонується як доступний для всіх розробників, на практиці вимагає добре узагаленого базового рівня програмування та математики, що може відсеяти частину цільової аудиторії. Також згадка про ранній досвід з Prolog та Lisp підкреслює, що фундаментальне розуміння парадигм важливіше, ніж конкретний фреймворк. Це наполягає на важливості теорії, а не лише практичного коду.

Опис відео

Welcome to TensorFlow from basics to mastery. Some of you may have taken deep learning or machine learning from me and learned about the amazing things you can now do with deep learning and machine learning. One of the best tools you can use to implement these algorithms is TensorFlow. Learning algorithms can be quite complicated and today programming frameworks like TensorFlow, PyTorch, Cafe and many others can save you a lot of time. These tools can be complicated and what this set of courses will do is teach you how to use TensorFlow effectively. In order to teach much of these causes, I'm absolutely thrilled to introduce Lawrence Moroni. >> Thank you, Andrew. >> He is a developer advocate at Google and has been working on Google AI and TensorFlow. Uh Lawrence has also written over 30 programming books, including four sci-fi novels. >> Yeah, exactly. I've I I've been busy. I I really enjoy writing, but the one thing I enjoy even more is like learning and teaching AI. So, and actually I've learned from the specializations that you mentioned and I've learned from your courses. So, it's a real honor to be here with you. >> Oh, thank you. I I I did not know that you were taking my courses as well. Thank you. >> Ah, definitely. So, it's a big fan and that's really I what got me into AI was um it's actually a long story. I started doing AI many many years ago back when it was things like prologue and lisp and all that. But now when we've gotten more into machine learning and deep learning with neural networks, I needed a place to learn it and I actually learned it from your courses. So it's it's been exciting to be actually coming full circle and now teaching it myself, too. >> Thank you. I actually did not know that. So thank you for sharing that. >> I caught you by surprise. >> Yes. [laughter] >> So it's like uh where the industry is at right now is one of the things that like really excites me because it's like it's just it's it's really it's exploding, right? there's a deep learning and and machine learning skills are becoming ever more important and opening up whole new scenarios. >> One of the strange things and exciting things about machine learning in AI is that it's no longer just a technical thing limited to the software industry. So that everyone in or at least every industry needs to figure this out. >> Yeah. Yeah. And it's um exciting from a developer perspective because it's there's a new paradigm and that kind of and the new paradigm to me is opening up scenarios that weren't previously possible, things that were too difficult for me to write programs for. And so and whatever it's like whenever a new paradigm comes and these new tools come and you can open up new scenarios, then that opens up great new opportunities. >> Yeah. And I think um one of the tragic things today is even though the whole world sees their promise and the hope of these machine learning and AI capabilities changing so many things, the world just doesn't have enough AI developers today. >> Exactly. I mean there I've seen surveys of like you know 25 26 million software developers and like maybe 300,000 AI practitioners. So part of my personal passion is to try and turn like those 24.7 nonAI practitioners, a significant portion of them into people who can understand AI and who can build the the new and exciting things that we can't think of. >> Yeah. So I think if you finish this set of courses and learn how to code in TensorFlow, hopefully that will help you do some of this exciting work and maybe become an AI developer. So in the next video uh you'll hear Lawrence talk about the differences between traditional programming paradigms versus the machine learning and deep learning programming paradigm. And you also hear about how to fit an Xtoy data relationship, how to fit a straight line to data. So please go on to the next video. Thank you.