How To Self Study AI FAST
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- Опубликовано: 29 дек 2023
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A video to learn AI skills for my short attention span friends who keep giving up on learning this field.
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It's about learning, coding, and generally how to get your sh*t together c:
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🖱️Links mentioned in video
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Freecode camp for python: • Introduction to Progra...
Python book: automatetheboringstuff.com/
Introduction to AI: • [1hr Talk] Intro to La...
Prompt engineering course: www.deeplearning.ai/short-cou...
Josh Starmer: / @statquest
Math for Machine Learning: imp.i384100.net/math-for-ml
Stanford Statistics: www.coursera.org/learn/stanfo...
Brilliant Neural Network course: brilliant.org/courses/intro-n...
Brilliant Intermediate Deep Learning course: brilliant.org/courses/artific...
Deep Learning Course: • Happy Halloween (Neura...
Deep Learning Specialization: www.coursera.org/specializati...
Computer Vision Specialization: www.coursera.org/learn/introd...
Natural Language Processing Specialization: www.coursera.org/specializati...
Beginner project with starter code: github.com/fiverrhellotinah/y...
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🎥 My filming setup
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⏰Timestamps
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00:00 intro
📲Socials
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instagram: / hellotinah
linkedin: / tinaw-h
discord: / discord
🎥Other videos you might be interested in
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How I consistently study with a full time job:
• How I consistently stu...
How I would learn to code (if I could start over):
• How I would learn to c...
🐈⬛🐈⬛About me
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Hi, my name is Tina and I'm an ex-Meta data scientist turned internet person!
📧Contact
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youtube: youtube comments are by far the best way to get a response from me!
linkedin: / tinaw-h
email for business inquiries only: hellotinah@gmail.com
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Head to brilliant.org/TinaHuang/ to get started for free with Brilliant's interactive lessons. The first 200 people will also get 20% off an annual membership.
chili sauce
what keyboard are you daily driving tho
nice job ,well done we need more of you's ,Tina is sisters name
Self Study AI Checklist:
Beginner
1. Learn Python
--- APIs (v. imp)
2. Learn LLMs
3. Learn Prompt Engineering
--- Make a Project (Chatbot, Image Generator, etc)
Intermediate
4. Learn Python libraries:
-- Numpy
-- Pandas
-- Matplotlib
-- Scikit-learn
5. Learn Maths:
-- Calculus
-- Linear Algebra
-- Probability
6. Learn Statistics:
-- Inferential Stats
-- Descriptive Stats
-- Hypothesis Testing
-- Central Limit Theorem
-- Distributions
-- Confidence Intervals
As a CSE grad, most and maybe all of these were taught but my brain was not braining. Lets see how long can I go this time.
I dont know if this will be of help tbh, but good video Tina ☺
Where I an learn llm's
@@visual_punch internet
Lovely and insightful video !! Thanks for sharing out Tina☺
It's a very useful video, first you feel frustrated and lost, but after viewing it, you clear understand AI and get ready for a real work. Thank you and happy next machine learning year, 2024.
This has got to be one of the best road maps I've watched yet... thank you for this, as an networking infrastructure guy, I'm ready to pivot before they come for my job...
Hey, that's me...a distracted, short attention span learning. Thanks for this.
thats me too! oh look a squirrel!
Stop doing stuff that require short attention span .. like reels , yt short...
Trust me it works ..
that's us buddy you are not alone in this
🎯 Key Takeaways for quick navigation:
00:00 🧑🎓 *Introduction to Learning AI*
- The video introduces the challenge of learning AI and sets the stage for a new learning method.
00:57 🎯 *The Renon Method*
- The Renon Method is introduced as a way to learn AI incrementally by starting with the basics and progressively building knowledge.
- It involves creating small AI projects to maintain motivation and enthusiasm.
02:07 🤖 *Understanding Machine Learning*
- An explanation of machine learning is provided, focusing on pattern recognition in data.
- The example of a hot dog or not-hot-dog model is used to illustrate machine learning.
03:48 🍕 *How AI Models Learn*
- Details on how AI models, such as the hot dog or not-hot-dog model and ChatGPT, learn from data.
- The concept of probabilities and predictions is explained.
05:13 📚 *Learning Python and APIs*
- Recommendations for learning Python basics and using APIs to interact with AI models.
- Resource suggestions for Python learning are provided.
07:07 🔢 *Learning Math and Statistics*
- Guidance on essential math and statistics topics for AI, including calculus, linear algebra, and probability.
- Resource recommendations for learning these subjects are given.
09:00 🧠 *Deep Learning and Specializations*
- Introduction to deep learning, neural networks, and specialization areas like computer vision and natural language processing.
- Resource suggestions for deep learning are shared.
11:25 🛠️ *Building AI Projects*
- Advice on starting to build AI projects, contributing to open-source AI models, and fine-tuning existing models.
- Emphasis on practical application and not getting overwhelmed with too many resources.
Made with HARPA AI
is this your own ai work?
Best video I have seen about AI so far. Your video gave me a light when I was lost in the sea of AI.
Thank you, this is such a fantastic video, full of great resources and really well presented 😊
I just want to say thank you so much! This is the most valuable video I have ever watched on RUclips. Especially including Coursera courses is very thoughtful of you
I've seen just a few videos of yours come across my feed. I have to say they were very helpful. I have a goal to learn AI using free resources and you give a great outline for people who want to learn. Thank you and keep the videos coming!
Yes definitely make more videos like this! Extremely helpful ❤️
I've been looking for a video like that for so long! Thanks you Tina 👌🏻
Tina, you're a brilliant human being and DS. The best is yet to come. Happy 2024! Thanks!
Super helpful roadmap for learning AI. Your teaching style is fun and amazing. Thanks! ❤
Best video on ai so far on RUclips...now you got me interested in self learning ai and you just earn a subscriber.🎉❤
AWESOME details and breakdown, THANK YOU, YES some of use have ADD but are great IT engineers. learning a vast new tech is an uphill with a weights on, thanks
Great, great video with so many actionable resources. Lots of notes taken. I also really appreciate the disclaimer at the end on not treating this vid like a to-do list of all the things we've got to learn. Soothes my perfectionist demon.
I am planning to take machine learning next semester in college and I hope this is a helpful video.
“Prime numbers are what is left when you have taken all the patterns away. I think prime numbers are like life. They are very logical but you could never work out the rules, even if you spent all your time thinking about them.”
― Mark Haddon, The Curious Incident of the Dog in the Night-Time
Thank you for giving us a starting point. Please do lot more videos on AI
you had me at Rinnigan :D I love taking concepts from my fav stories and applying them to IRL concepts
You're my favorite AI RUclipsr. Your pedagogy is unmatched.
lol
TYSM Tina, this was so well done!
Hi Tina, thank you for sharing. Keep up! ☕🍹
Thank you!! :D
@@TinaHuang1 i will say on this video Happy New Year Tina .Love you!
Awesome! I have learned a good portion of the recent ai and don’t have a short attention span but still loved this video 👍😎
Will you please share what you learnt, i guess you deep dived into deep learning, genAI, LLM, langchain, stuff, pls share your experience, i wanna get my hands into it, what to do? I have basic mern experience...
Thanks for the concise explanation and direction for getting started learning AI, really appreciate it.
Thank you! I'm pretty sure I'll spend some time coming back to this video.
New sub here. This content is perfect for what I was looking for. Thanks so much, Tina 🙂
Automate the boring stuff is an amazing book. Provides alot details while not having the reader become bored after few pages.
being able to study things that you find boring to read is a key skill for being a good student tbh
@@myname-mz3lo True, but not the only way to go about it. I learn a lot more when my brain is more engaged with the things I'm learning. If I can learn the same things, but with more dopamine, I'm going to do it that way.
Definitely interested in more AI/ python videos 💯
First Video that actually makes sense to me thank you so much
Great content Tina thank you for all your hard work and involvement to provide us such a clear vision of AI & DS 🎉🎉
I love your videos. This was extremely helpful. Thank you!
I was interested before but now thanks to you, I have a path. Thank you again.
Your videos are sweet. Thanks for this awesome content! It's very engaging and straight-forward.
I like that you put difficult things much simple about learning AI .Hope you make more of them
How do you learn bro?
Wow thank you so much for the resources!! Currently finishing up a DS bootcamp and was worried about how I can re-learn the stuff I didn’t fully understand with only the bootcamp material, so this is seriously perfect
Definitely trying to learn AI thank you for your wonderful videos, please keep on making them ❤
You just described how I learn.. but I've always referred to it as the jump in head first method, lol. Thanks for putting a name to it!
Love this.
Would like to get into ML/AI for genomics. Thanks for sharing your thoughts on self study.
Thank you so much for this video! Yes, I would love to learn AI and I hope you can make more content related to AI and learning it. Thank you!
but girls think AI is very hard tho
@@jake9854 I like a challenge. If you have interest in something and want to learn, hard won't stop you. I think if the girls you are referring to think it is too hard and lose motivation and desire to study it, AI was never their cup of tea. I am sure they will enjoy another subject.
This method is really cool! Also the little baby model emoji! Love ittt! ❤❤❤
How interesting! Many musicians also start off by playing stuff they enjoy from original creators, and then start approaching music theory and diving deeper
I'm definitely going to give it a shot. I'm 60, but what the heck. You're videos are the best, because you take viewers by the hand, and guide them step-by-step on what they have to do, to get from clueless to competent.
Some clarying points and addiontal tips, espeically for job seekers:
1. You only need basic math, you do not need to know a single thing about calc, linear alebgra, etc. if you are not going into research. Of course, the more you know about this the better, but if you just want to make AI products you don't need it. Research is a completley diffrent ball game where you will need at LEAST a master's to so much as get your foot in the door. It also doesn't pay as much as applied jobs (like Machine Learning Engier, Data Engineer, or even higher level Data Analysts). Optional: if you want to skip calc/differnation etc. that's fine but I strongly recomend learning linear algebra as it will make your life much easier. Again, you don't need it to get by but if you're looking for work in this espially it will make your life much easier.
2. The type of statistics you need are APPLIED Statstics (this includes all the types she is talking about, this is more about the rigor you are learning it). If you are going into research you might learn these from a more rigours perspective where you would need to already understand concepts from Calclus and so forth.
3. If you're looking to get a job, a realvent masters defeinely helps but so do quality Certifcations. So Micrsoft, Amazon, Google should be your starting points. ONLy get them from the websites themselves, do not get them from Cousea, Udacity etc. It's okay to learn from these sites, just dont' put them on your resume (some places are okay with this)
4. Get good at programming, espeially in Python. The following is optional: I would also take courses (online for free is fine) in Discrete Math, Data Strucures (a proper course, not just learning what Python's are) and Some kind of Algohrims Course. These will help you problem solve as you need to figure out how to implmeent things. Again, you can get away with not learning this stuff, but it will make your life easier.
P.S. This is all just my opinoin from being in the job market and working. I encourge you to do your own resarch.
Awesome. So helpful! Thanks Tina!
Your skin is so good! Can you share your skin care?? 🤩
Thank you for wonderful video and book's recommendations
Thank you, great video 🎉
That scene where Jian Yang (not Jimmy) built a binary classifier (hot dog vs. not hot dog) but everyone else was expecting a multi-class classifier was absolutely hilarious. Don't forget he ended up selling his app for millions!
Thank you Tina. Learning AI seems doable now.
Thx Sis for info......... It will help me in my final year project...
This was dope. Great Job!
The editing was great in this video 🤌🏾
Loved it. Please come up with such great videos
Already a web dev but starting the Stanford Machine Learning Specialization on Monday. Been brushing up on Python for the last couple of weeks but it came back pretty fast. Let’s go!
Thank you! My attention span is abysmal. Please keep the content coming. You are awesome!
To be honest, man. I have checked out sooo many videos, completed the machine learning/deep learning specializing on coursera. I am bad at math but have a basic understanding of some concepts (i am on 2nd year of a CS degree). But the thing that i really think there is missing - including on literally college courses, is putting things into practise. I think there are way, way way too much theory before you actually can get your hands dirty. I think it would be so nice to just have courses teach you how to implement these things instead of writing out endless equations. It exhausts me so much, because even though i learn some theoretical stuff then i don't even know what to remember or why. When i finally sit down to try and do something, i'm like "eh, what now?". If you just learn how to implement, say, a neural network to perform a task x amount of times so you could actually do it and see some results, then you can learn about the under the hood stuff as you go or after, so you can actually relate it to concrete situations.
that is not the purpose of a CS degree though. CS degree prepares students for the ability to do research and self-learn the applications. All those fancy models are built by equations, and you need to know that math to build the next generation of stable diffusion or large language models. If you don't understand the math, then there is not much that you can build for complex neural networks. Learning how to call APIs and apply pre-trained models can be learned on RUclips and that is not worth the tuition.
Thank You! You're great and I love your video!!!😊
WoW the road map i create and do it myself is just like you. Except that i comment out some math that learn in U. Great tips!
Thank you!
THANK YOUU👌 you literally carry me 😊
Thank you very much for the information and I wanna learn AI please make more videos about AI
Awesome video!!!
Your videos are really encouraging and easy but I will like to learn more
Whoa!!! You're really smart, I am really loving your videos
I still remember watching your video where you created years ago doing Twitter Analysis of Leonardo di caprio case. Learned a ton of stuff from that one video than hours long video courses.
Could you please make more such videos!? 🥺🙏
Great introductory video in the world of AI with great delivery also.
i'm generally interested in a lot of things without being an expert in one thing. But you sold me on this. Also, your pathway is very interesting and never thought of it in that way. Your content is a gold.
Thanks for the guidance
Wow this is pretty useful and clear, thanks!
Appreciate the very good video. As a long-time educator, learning by successive refinement is a well-known teaching technique. I use it to teach IT concepts, advanced AI/ML, and even guitar. Most universities' curriculums are structured this way with "course levels". Btw, LLMs do not use CNNs. They use transformers.
Great content! Please do more in-depth on your learning path 🎉
This is one HOT Tina modeling video about AI.
Thanks
omg I love you! thanks for this!!!!
Thank you Tinaaaa! Also you're glowing lol
“We become aware of the void as we fill it.”
― Antonio Porchia
This was awesome. Yes, more learn ai content. I'm an elementary teacher and I have to differentiate my lessons daily.
I am sooo excited to try all these. 🤩🤩🤩
I appreciate the knowledge and the experience you are spreading Tina.
I got confused and lost in this field, I don't know where to start there is a lot to learn and a lot to consider. It gets overwhelming sometimes.
My real question is what is the real starting point? I'm spending a huge amount of time learning different things and at the end, I see myself knowing nothing and cannot relate all the things I learned together, I know Im missing something but I really cannot understand what it is.
Very nice. Good information and resources thanks.
Hi there, i have a even better strategy. I start always with one of the outer rings of that rinnegan ring and just read a little to see what my final goals look and feel like. Then i deal with the more inner rings and do the same. And then i deal with the outer ring (your initial one) and handle that but also another one, since the first rings are often the most boring. I did that with learning french. I started with Level C1 (C2 being the hardest) and moved downward….😅
Thank you for your breakdown. I keep being asked how and what AI is all the time. It helps to hear how others explain a complex and wide topic like this. I work in the weeds in code and have a hard time explaining things in Human.
I saw the vectorizing of parameters like words in an LLM and was fascinated by this idea... Someone asked how can a vector describe if something is close... So I broke it down to a small set of coordinates (the simplest vector of 1) by using latitude on a world map. Shorter distances can be evaluated with simple math, and then add another parameter (Longitude) and now the 2 parameter model of Lat/Lon is born :D... Does that resonate with your definition? My non-math/non-gis friends occasionally have a hard time with that idea still...
My AI battle cry is "BreadDogPig!"
Thanks a lot :-) this one was useful.
realistic and detailed roadmap 🔥
You’re so awesome thank you so much!
i lost it with the rinnegan method 😂😂😂 god, i love how you mix anime stuff with more serious content. thanks for doing it
Loved it all. Did you leave Kaggle out on purpose?
When you think about it all of us are LLMs. It's how we've learned to speak whatever language we speak and whatever other languages we speak. it's also how we do everything that we do by predicting what comes next based upon what we've learned in context. That's why there can be cultural similarities and differences. Because of the different models learned by different experiences. Culture and language are model sets in themselves.
Great video! It's one thing to learn ML on your own, but can someone really land a job after being self-taught? In your case is was part of your Masters education (kudos to accomplishing that with a short attention span 😜). I say for anyone who's wants to learn ML for reasons beyond just landing a job, it can be exrtremely rewarding if you have an endgame in mind (eg building your own product).
Love your video first time watcher here, yet I have to say babies are super engaged and smart you just have to nurture them ❤ (my son was hours old and he was smiling and kept taking off his mittens which are placed on babies when they are first born to protect their tiny hands and to keep them from scratching their cute little face, he had plenty going on in his mind, he’s 2 now and is learning 3 languages) idk what were in those Covid vaccines but these post Covid babies are wild 😂❤
Thank you
I like the idea of excitement and satisfaction to motivate you.
Huh? I didn't know about the rinnegan method. I appreciate the insight. No wonder I had a hard time in school.
I think the problem is a lot of us with ADHD/Autism require a top down approach. Neurotypicals tend to be fine learning in a straight lines but I think a lot of us NDs would rather learn the big picture first but no one teaches it that way.
Standard Education would teach you about a car by teaching you about, the invention of the wheel, the axle, carriages, internal combustion engines, assembly line, yada yada yada.
That would be so lame to learn about cars.
I would start with what a car is and is used for so we have the why. The rest just shows up because of natural curiosity. How does it move? It rolls on wheels. How do the wheels spin? Engine. What's an engine? A thing that blows up fuel and spins a pole. What happens if something gets stuck? We use Oil to keep it lubed.
One method is straightforward and boring but makes sure you know the topic from the ground up. The other method is rapid, exciting, and intuitive but can leave gaps. I am a firm believer gaps are easier to fill, compared to the long task of perfect foundational knowledge.
Hi Tina. Thanks for your video.
Could u give me some advice, my job is cfo so i m an expert in accounting and finance, but only 10/100 about AI and everything.
What should i be focus in if i want AI support me to do some basic tasks for operating a company and then i like to create app , or APIs to advance the task !!
Thank you Tina. Please can you make a video on generative ai?
“Everything you can imagine is real.”
― Pablo Picasso
You are a gem, Tina