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@@Alfie-x4c You should have received an email with all the study materials - I’ll look into this and send you an email!

@@myliu6 PPO is essential! Thanks for the support :)

hi , i bought your, First-principles framework, but i cant see how to go to that website, i cant find the the study materials, it says subscription active(green)

Unfortunately, I have no numbers to present, only potential lines of thought to explore. ================================ With the invention of circular saws, jackhammers, and power drills, did the construction workforce dwindle to a barely noticeable group? Or did society then start building urban sprawls and lone skyscrapers? Doing in mere years what Classical builders needed decades and centuries to match. (The Temple of Zeus and the Pyramids of Giza remain awesome and impressive, yet we can build higher or bigger in a fraction of the time.) When chainsaws were invented, did we lose lumberjacks, or have more logs than we knew what to do with, along with requiring more efficient ways to transport them en-masse? Instead, we needed limits on what trees to cut, what forests to keep untouched, and how many to re-plant, just to remain sustainable. And the cranes and forklifts and heavy machinery came first, before the certifications and specializations that construction workers could branch out to. Meanwhile, there remains that artisanal craft that remains fundamental, that needs to be passed down. What good is a carpenter who can operate a hydraulic press, but couldn't level a shelf on a wall to save their life? Ditto, then, with robots, both physical and digital. For here, Grant highlights the misconception: the businessman believes that bots can build whatever they prompt, and can soon do away with the imperfect, unnecessarily expensive flesh-bag. The software engineer (or construction worker, or lumberjack, or etc.) should *remember* that for every single thing that goes wrong with the bot, the businessman has barely any clue how to fix it. Yet, the experienced worker has almost two dozen ways, depending on cost, time, building code, regulations, company policy, oh and it looks like your bot also shunted the foundation, that's another 450k right there--- On top of that, to repair malfunctioning bots requires a software engineer who can find where things went wrong AND modify the code to make things right, assuming the problem is *entirely in* the software. That sounds like additional cost. That sounds like additional training. That sounds like one extra point in your salary negotiation. And much like everything related to professions and careers, how you leverage your skillset, experience, and expertise is primarily up to you.

Not necessarily. Yeah there's 3x the productivity but the complexity of applications will only grow further. So, even in the pessimistic view, it's not going to get any worse.

@@noctis_rune you cannot say that with certainty, there are to many aspects and variables at play, the fact that this has happened in the past does not mean it will happen now.

I appreciate it man

Glad you had a good experience! Let me know how you like the DL specialization.

exactly 💯

100%. Read, Implement, Repeat.

I appreciate the support man :)

so can you recommend books and also how we can affectively use it 😊

Will definitely add this to the queue of video ideas! Thanks for the suggestion :)

All resources in this video (including the ML Programming questions I created) are 100% free.

when is the next sale for your First-Principles Framework (Learn Fundamentals) ?

Yes that’s exactly what Industrial Revolution did, decreased jobs

Not necessarily. If a company is more productive, they can tackle bigger features and expand their product line. That all requires hiring more engineers to complete that work. So product teams may be smaller, but the company itself may be just as big if not bigger than today.

Much appreciated!

Best of luck man! Let me know how it goes.

It's definitely a tough decision to make! I also find model architectures to be one of the most interesting parts. Let me know what you decide!

i dont think it's needed. it just talks about activation functions and backpropogations.

simply i would need this for one reason 🤷

No need to memorize these equations!

​@@gptLearningHub bro simply i need this so if you know where i cand find this please share

does your website is down rn?

@@errrbrrr3821 Shouldn't be! Let me know if you still have any issue!

Here it is! ruclips.net/video/bbYdqd6wemI/видео.html

A bit behind on emails - will get back to you soon!

​​Plz do...i would really appreciate a response@@gptLearningHub

@@yudatriananda3558 Appreciate the support man, I’ll keep this in mind for future titles

@@aravindudupa957 It would help since you’d gain a deeper understanding and seem more credible to investors than someone with no master’s or phd, but definitely not required!

@@vv_vv7992 That’s true, Linear Regression must simply be linear in the parameters, but nonlinearity in the variables is allowed. More accurately, without the nonlinearity, the “Neural Network” collapses into standard Linear Regression and we effectively lose all the hidden nodes. Thanks for your comment!

@@ab8jeh But without the nonlinearity, the Feedforward network collapses into standard Linear Regression, and we effectively lose the hidden layer!

Go over universal approximation theorem if you want to find the real reason of using activation functions

Hopefully not!

All research work is done in Pytorch and recently pytorch has increased compatibility for being used as the main workhorse for production level AI/ML applications (previously Tensorflow was the main choice) because of things like Torchserve, ONNX etc. And its very easy to use it with Hugging face transformers which is the main requirement in industry to use pretrained models to solve problems.

@ i mean the video promised to discuss this unfair advantage. Instead we got another pytorch tutorial