Andrew Ng Machine Learning Career Advice
ฝัง
- เผยแพร่เมื่อ 15 พ.ค. 2024
- Hi, my name is Jared Beckwith. I’m self studying artificial intelligence, machine learning, and deep learning. In this video I’m sharing a clip from MIT professor Lex Fridman’s video, “Nuts and Bolts of Applying Deep Learning (Andrew Ng)”
The question Andrew Ng answers in this video is, “How do you build a career in machine learning?”
Andrew Ng is an adjunct professor at Stanford University (formerly associate professor and Director of its AI lab). Also a pioneer in online education, Ng co-founded Coursers and deeplearning.ai. With his online courses, he has successfully spearheaded many efforts to "democratize deep learning" teaching over 2.5 million students through his online courses. He is one of the world's most famous and influential computer scientists being named one of Time magazine's 100 Most Influential People in 2012, and Fast Company's Most Creative People in 2014. - วิทยาศาสตร์และเทคโนโลยี
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Amazing to hear Prof Ng speaking about AI career! The secret seems to be consistency again!
Been following up with Coursera and there’s so many subjects that our educational system even since secondary school doesn’t correspond to our realistic life with particularizing skills and values. So much an awe-inspiring for an extraordinary human being like Andrew Ng whose capable to help those who unfortunate to pursue education in spite of the fact that education learning doesn’t end when you graduated, it’s sustain in a long-life learning process, which Andrew offers to those who need an extra opportunity to pursue free learning in Coursera. Thanks!
I cannot thank you enough for sharing this lecture, I got so inspired! I really appreciate people like you contributing to the community
Happy I can help!! 👍
Awesome. Andrew Ng is the only person I like to follow advice. He is a very genuine person. Seems to me. I admire him.
Very motivating!!! Thank you.
I love this autodidact movement!!!
incredible guy, thank you for sharing
What an insightful content, thank you so much
Key insights
💡The process of reading and studying multiple papers (around 20 to 50) can reliably generate original ideas in the field of machine learning.
📚"People often shy away from the dirty work in AI, but it is essential for success, whether it's downloading data, tuning parameters, or struggling to replicate results."
💡The combination of doing dirty work and reading papers is the most reliable formula for producing great researchers.
🧠The process of continuously learning and iterating is crucial for becoming really good at machine learning.
Hi bro what are multiple papers? what is papers ? Do you mean
@@vinaybanka7589 Research papers
Pro tip: You don't need a PhD to be able to read research papers.
Exactly!! I’ve learned so much from research papers even if I didn’t understand everything.
you do need to be math fluent though, and have an extensive math background when it comes to ai papers
Yep. Only the reverse is true
You just have to be thorough with advanced undergraduate math courses like Measure theory, Measure theory based probability and advanced linear algebra.
U just need chatgpt account and som engineering prompts skills now
Thanks for the lecture man. Great help
Glad I can help my friend! 🙏
That's very well said and very solid advice. Thanks for sharing!
Thank you for your comment. I’m glad I could help!
I want to build my career in ML. But i dont know how.
And thank you so much for sharing this video. It give me some ideas about generating new ideas.
great advice from my hero in AI thank you so much!🙏
I’m a big fan of him too, glad you enjoyed my friend!
cool video Jared Beckwith. I crushed the thumbs up on your video. Keep up the solid work.
Appreciate it red beard!
Thanks for sharing sir. 😍😍😍
Thanks for watching and commenting 😊
Very good advice
Thank you
Thanks for watching Amit!
inspiring. Thank you
Thanks for watching!
thanks for sharing
Happy I could help you!
Thanks bro🖤🖤🖤🖤
Thanks for watching man!
Thank You
Claimed, thank you
No problem !
First we should start ML , DL and then AI is
it right path 👍
Very inspiring.
Thanks Suhail!
Andrew Ng is a gem!
One of my favorite Ai teachers!
Bald guy in the front row is Pieter Abbeel, professor of AI in UCB and former PhD student of Andrew Ng. Pieter Abbeel has an amazing h-index of 153.
Nice
Where will I get the research papers
Thank you for sharing , Does any one knows of a good site to follow for ML papers as suggested?
I have the same question!
Google is how I find good papers. Use ML keywords like “convolutional neural network” if you’re interested in computer vision for example.
Here is a paper on a neural network that is trained to recognize different clothing types: arxiv.org/abs/1708.07747?context=stat
Paper with code
Those are some good advice for someone who already knows some ML stuff. What about the guy just starting? Any suggestions on what kind of courses I should pursue?
When I was brand new I watched this intro to deep learning video by MIT professor Lex Fridman. He leaves out a lot of complicated math/code and gives you a basic overview of the field.
Here’s link: th-cam.com/video/O5xeyoRL95U/w-d-xo.html
@@jaredbeckwith thanks!
@@jaredbeckwith He will be doing new lectures next january
@@jaredbeckwith Lex Fridman is not a professor. He is a researcher and he has nothing to do with MIT.
This is the more convaincing presentation I watched related to this subject with use-cases. Yes AGI (artoficial generative intelligence) is useful, whereas there are some risks officials must do their best to minimize, by using appropriate regulations. Thanks à lot.
Learning ML, then DL then AI then what comes next...?
Read a lot of papers (do a lof of projevts)and work on replicating results
At 20 or 50 papers you'll start having own ideas
reading ml papers wouldn't be so intimidating if they didn't throw out super math dense gibberish, tensors, linear algebra, statistics, diff eq etc.... take into account andrew ng's background an excerpt from andrew ng's Wikipedia page *"In 1997, he earned his undergraduate degree with a triple major in computer science, statistics, and economics from Carnegie Mellon University in Pittsburgh, Pennsylvania, graduating at the top of his class. Between 1996 and 1998 he also conducted research on reinforcement learning, model selection, and feature selection at the AT&T Bell Labs.[10]"* No wonder ai came so easy to him he is a genius who is also extremely math fluent.
Yes, papers would be much easier to read if the authors cut the math gibberish and knew fluent English.
“Came so easy to him” You mean mastered the necessary topics for over 20 years? That’s definitely a weird way to phrase it. If lacking that mastery is holding you back from mastering ML, then do the 4 year degree for the same topics. And it’s not just math gibberish, machine learning IS math. If you want to make discoveries in ML, not everything will be in PyTorch. The idea and math behind ML has been developing since 1943. Andrew Ng didn’t wake up one day and discover ML with the relevant skills conveniently sitting in his lap, he just worked very long and hard. Don’t expect one Python crash course to match that.
What to do if you have 1 year for research
How many weekends do I need to sacrifice to be a ML researcher? :')
all of them
What is meant by replicate results?I mean practically speaking?
Some research papers will reference datasets they used to build their neural networks. You would use the same data and copy the neural network in the paper to get the same results.
@@jaredbeckwith i thought he means
follow the videos and follow them
imitate
does this okay ?
Do i need extensive maths for good ai ml engineer
You can probably implement the algorithms without knowing math, but a lot of employers will require advanced degrees
Where I can read papers ?
I Google research papers in my area of focus and most are freely available on the internet. For example for Ai, here is a research paper on convolutional neural networks: arxiv.org/pdf/1512.07108.pdf
hey need a bit of help i am a graduated chemical engineer and now thinking to study further in machine learning and data science but i almost know nothing of machine learning and i have to submit a research proposal in machine learning or data science can someone help me to chose a topic or how to find a topi thanks
If you know nothing about machine learning start by building a model to recognize handwritten digits. It is called the “MNIST dataset.” You take a dataset of human labeled handwritten digits and teach a machine to recognize digits correctly. There’s a lot of documentation on Google/TH-cam about MNSIT. Afterwards, you can build something like a Cats vs Dogs detector using the same techniques.
Read enough papers, replicate the results - 20~50 papers later, you will start your own idea.
Willing to do dirty work.
It is a long term marathon. - train our own brains neural networks to learn how to do it
what are multiple papers? what and which papers ? Does he mean can anyone can say me please? Im interested in Ai
Here’s one talking about using a CNN to detect handwritten digits: vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf
@@jaredbeckwith Thank you a lot for helping me!
@@jaredbeckwith can you make video on automation autopilot Machine learning for cars ? Guidance from A to z ? Please and recommended books for Machine learning on automation autopilot for cars ?
Can someone answer me what is replicate results?
Be able to copy the research paper and get the same results. For example, if a cats vs dogs image detector was 95% accurate in the paper, you should be able to get the same accuracy by using the same neural network.
@@jaredbeckwith Thank you
I have a life on the weekends
Work-work balance
a a a a a a
Please, do not be over enthusiastic. The complexity in this career and how messy are the software, will make you quit at some point.
Thanks Jared
No problem my friend!