Комментарии:
Good to mention this book is in Python and R!
ОтветитьYeah it's easier because now we can ask chatgpt
ОтветитьTeach your kids Python, you'll thank me later.
ОтветитьLol
ОтветитьAs a Uni student from a ML program, this ain't true guys, particularly when it comes to understanding all the Math magic behind it. But it's damn interesting and rewarding (at least for me)
Ответитьmost tech bros think ML is only neural networks not statistical learning on tabular data.
ОтветитьNice
ОтветитьRead this in college
ОтветитьThanks hoggman
Ответитьhahahaha so basically do what should be done since the invention of the book, copied
ОтветитьLet me pick up C++ for it 👨💻
ОтветитьThanks for inspiration!
ОтветитьNow this is an actual good way of learning, i see so much python ml developer doesnt know what behind all those class and function. Everything is abstracted that made us not learning just using.
ОтветитьMath I can't escape it can i
ОтветитьLinear regression is an incredibly simple algorithm, that is not proper machine learning the way we think of chatGPT and DALL-E! Anybody that knows calculus and very basic linear algebra can learn linear regression in a couple weeks, the actual hard part of machine learning is when the problem is difficult enough to warrant an algorithm that requires training. It's understanding how a neural network is optimized, or how a transformer works, or what diffusion is, that's the part of machine learning that has all the opportunities, and you really do have to go to school to learn that. This stuff for comparison is something that is taught in high schools even. It's quite literally something you can teach to a teenager.
ОтветитьStep 1: Name 10 books
ОтветитьSays to learn machine learning - recommends statistics book - shows linear regression.
Lmfao
If you think you can learn ML with one book, I have bad news for you...
ОтветитьComon bro it is easy pff
Ответить🤡🤡🤡
ОтветитьPopping in a big thanks here since I was looking for a starting point in machine learning. This + the Google Courses will be great. Python - here I come.
ОтветитьThat’s just learning how to run code on simple and overused benchmark data
Ответитьspss is it?
ОтветитьI have studied data science but I don't have any certificate, can I get a job?
ОтветитьML is dead, dont waste your time
ОтветитьHow to learn :
1) get the book
2) learn what's in it
3) let the machine knows it's cat or a dog 😂
Machines Learning Machine Learning.
ОтветитьFavorites language
Javascript 🤔
Test
Ответитьhey @GregHogg what is this vsCode Theme please?
ОтветитьWas this code created for personality test?
ОтветитьSir where can I learn scikit I searched in you tube but they are only few and its not suitable for me
ОтветитьSaying easy is wild
Ответитьgoing to heaven has never been easier.
Step 1: look at the sky
step 2: be in heaven
Where can I get it free?
ОтветитьESL (elements), is a bitch even with someone with a bit of a background, i spent oh idk 8 hours on a covariance SVD proof for like decomposing effect.
Could be harder or easier than i remember, its really just about mathematical patterns. ISL is good, but completely trivial
Awesome
Ответитьthanks
Ответить❤
ОтветитьMachine learning
ОтветитьI didn't even understand a word you said.
ОтветитьAnd that is why
ОтветитьCode literally makes 0 sense like yall just be pulling those letters an numbers outcha ass
Ответитьyou have the most programmer voice if ever heard
Ответитьif you have a stronger math/stats background I would instead suggest murphy's probabilistic machine learning (vol 1 and 2). I work in ML research and to this day I still reach for that book when I need a refresher on a topic
ОтветитьIf I learn ML can I also create YT shorts too?
Ответитьte quiero<33
ОтветитьI'm finishing up these lovely courses in my master's rn it was a journey. Our prof didn't want us to just import torch or premade models from huggingface blindly, he made us rewrite entire libraries for fun to show we actually understand it. (Of course with a lot of scaffolding code) Made me appreciate algorithms much more - the dp and greedy from implementing Beam search, deriving the back propagation got me to learn how there are so many simultaneous matrix multiplications which GPUs with CUDA can help digging into its architecture :) I still won't recommend the course tho lol - unless you like to flat out work 24/7
Its nostalgic because I remember watching this exact video the summer before starting said masters program