- Видео 25
- Просмотров 1 297
Jacob Sapir
США
Добавлен 17 апр 2021
I started this channel because I was tired of the opaque, jargon-filled world of technical education. After years of navigating through convoluted theoretical explanations with zero real-world application, I decided it was time to stop the madness and simplify learning for everyone.
Most of my content-99% of it-is free. I believe in sharing my knowledge widely and only occasionally suggest my paid online courses for those who want to dive deeper.
My mission through this channel is not just to educate, but to empower you to make substantial changes in your career. I invite you to join our community of proactive learners, to embrace these tools, and to transform your professional life starting today.
Together, let’s build the skills that matter, minus the nerdiness.
Most of my content-99% of it-is free. I believe in sharing my knowledge widely and only occasionally suggest my paid online courses for those who want to dive deeper.
My mission through this channel is not just to educate, but to empower you to make substantial changes in your career. I invite you to join our community of proactive learners, to embrace these tools, and to transform your professional life starting today.
Together, let’s build the skills that matter, minus the nerdiness.
Making Perishable Efforts a Relic: The End of an Era
If you want to join my community and get exclusive updates, click here:
👉 training.realtechnologytools.com/my-contacts
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Просмотров: 14
Видео
Starting Your AI Journey? Learn the #1 Mistake to Avoid
Просмотров 819 часов назад
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Tools for AI Learners: Distinguishing Science from Charlatanism
Просмотров 714 дней назад
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Low Code AI: A Journey to a Weird Destination
Просмотров 2421 день назад
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The Truth About Prompt Engineering: When Everyone Misses the Mark
Просмотров 22228 дней назад
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My 4 Problems with To-Do Lists and How to Fix Them Today: Stop the Madness!
Просмотров 18Месяц назад
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How to Get Started Learning Python: Focus and Simplify
Просмотров 21Месяц назад
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How I Learned AI: Are You Overeating?
Просмотров 17Месяц назад
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How I Got Paid as an AI Expert Without an AI Degree: My Story
Просмотров 23Месяц назад
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Should I Adopt AI? What No One Tells You About Trusting Tech
Просмотров 10Месяц назад
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Should I Learn AI in 2024? Unpacking Outdated Practices
Просмотров 98Месяц назад
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Sacrifices in AI Learning: What’s Really Worth Your Time?
Просмотров 14Месяц назад
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Good and Bad Shortcuts in Product Creation: How to Avoid the Costly Ones
Просмотров 112 месяца назад
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How to Get Started Learning Python: Focus and Simplify
Просмотров 242 месяца назад
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Create AI Products Fast What NOT to Waste Time On
Просмотров 332 месяца назад
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Behind the Buzzwords: The Real Reason AI Sounds Complicated
Просмотров 82 месяца назад
Behind the Buzzwords: The Real Reason AI Sounds Complicated
Why You Should Avoid Math and Algorithms at All Costs
Просмотров 4802 месяца назад
Why You Should Avoid Math and Algorithms at All Costs
Why an AI Programming Degree Won't Actually Help You Build Products
Просмотров 333 месяца назад
Why an AI Programming Degree Won't Actually Help You Build Products
AI Vocabulary for Beginners: Structuring Your First Neural Network
Просмотров 513 месяца назад
AI Vocabulary for Beginners: Structuring Your First Neural Network
Why You Don’t Need a Programming Background to Build AI Products
Просмотров 333 месяца назад
Why You Don’t Need a Programming Background to Build AI Products
Neural Networks Explained: Why They Matter More Than Ever
Просмотров 413 месяца назад
Neural Networks Explained: Why They Matter More Than Ever
The Pitfalls of Pre-Packaged AI Solutions
Просмотров 283 месяца назад
The Pitfalls of Pre-Packaged AI Solutions
Should I Learn GitHub in 2024? A No Nonsense Guide
Просмотров 124 месяца назад
Should I Learn GitHub in 2024? A No Nonsense Guide
Rethinking Productivity: Lessons from the Real World
Просмотров 144 месяца назад
Rethinking Productivity: Lessons from the Real World
The Gap Between Academia and Practice: Why PhDs Don't Build Products
Просмотров 534 месяца назад
The Gap Between Academia and Practice: Why PhDs Don't Build Products
I hate to-do lists. This video was definitely helpful
What are some alternate IT jobs that don’t require learning AI?
I am learning AI and ML, but I like to learn the basics, am I not an engineer, am I a researcher??
you are AWESOME
How about someone who wants to write a phd paper ?
I took part in a uni robotics project, and for a while I was the only one writing code that worked in reality, while more experienced coders wrote untested drafts based on their experience(it was an autonomous robot project). They also really resented me as they were supposed to be more “qualified”. Anyway, reason why I’m saying this is because after the experience I went onto learning mathematical python a bit more deeply, but you reminded me that I did learn a lot simply from doing things practically, and also reminded me that I don’t need to “wait” to finish all the abstract stuff before returning to the practical.
Sigmoid is not used much these days. LLMs often use the gaussian - GeLU and for classification task you often use ReLU.. Sigmoid is slow. And ReLU and GeLU are easy to explain! 😊 If one wants to know how to use, one needs to have basic understanding of the activation functions ans when to use what based on their characteristics. Only needs an explanation of what non-linearities are and how they are mapped (embedded) into features and eventually single floating point numbers. Its all soooo easy. But its made sooo complicated by an industry that feeds itself high salaries just because they speak buzzword bingo alö day 😅
Okay, sometimes we do go deep into the algo :) But most time you can use stock library algos.. just what comes with pytorch
Without a programming background you can let ChatGPT generate those lines of code 😊 And yes, you are right; CS is overcomplex. It is possible to do all that with simple code. It's just people intentionally make it complicated because they want it fancy... And right -- Git you don't need too, if you are a lone wolf coding. There is local file system history in IDEs and the OS.. like Time Machine
The prompt usually is not chosen by the LLM. The prompt is tokenized, positional encoded, then passed to the normalization, through the attention mechanism, where its semantic context helps refining the prediction of the next token with semantic focus, then again normalized after it has been masked and softmax -- finally passed into MLP (using GeLU for captuning non-limearities) and eventually results in a logits list mapping to the tokenizer vocabular, so every token gets a log prob assigned. Softmax converts to %, topK and temperature acts as sampling and selection. Finally the next token is predicted and that continues.. all tokens including the last predicted on get refeeded (auto regressive automata) until a special end token is predicted. The prompt is always fed into the LLM directly. There might be a non-AI system in fromt that passes the user prompt plus context (loaded via RAG workflow, vector db) in a prompt template -- but eventually the LLM processes a defined prompt, not one selected by an AI usually.
Because they have to find new use.