This was a really, really good overview of their offerings! Having a no-code crash course is perfect for people wanting to understand what exactly HF do.
I absolutely loved this video. It was just what I needed. I especially liked that at the beginning, you said that this video was not hands-on coding because that's what I was looking for. I was watching while cooking, so I essentially needed a podcast, and this really nailed it
Really very very very very useful vedio for beginners ❤ really I was going to give up learning Generative AI but bcoz of your vedio I understand conseptually thank dear ❤❤ I was absent when Hugging Face was taught but I clear all things very well 😊 really appreciate ❤
It is extremely useful. Please continue educating newbies like me. I complement you for making this easy to understand introductory course. It is well paced and clear. Well done and thanks. Wish you all the success 👏🙏
@@1littlecoder I’m getting a PC with RTX 3090 to go deeper into stable Diffusion and training different models locally! Would you be able to advise me whether to keep the current windows 10 or reinstall latest Ubuntu ? Thanks
@@ysy69 I'd suggest you to post this question on fastai forum. The reason is there's an active stable diffusion course going on and a lot of people are building their own rig. So might get good suggestions
Very informative. Whish I found this a few months ago. Subscribed to your channel so I don't miss anymore. Will you be making an update of this Crash Course?
@@1littlecoder The hugging face website organisation has slightly changed so updated video might be good. Definitely should help your channel to bring in new viewers.
Great video @1littlecoder, thank you for doing this, it helped me understand what exactly huggingface is, the products and the problems they solve in just under 40 minutes!
@@1littlecoder Lets say i use hugging face to create storytelling content, and post on my youtube channel, is there any possible violation of property rights since the work is generated through artificial intelligence and language learning models...
Why did huggingface named their libraries as "Transformer" which is same as Neural network architecture "Transformer" . It is so confusing for beginners
Ah, created 1 year ago. That explains the gap 🙂 Still useful video, but I don't see any instance where we would use GOFAI models created by random people in favor of GPT-3.5-Turbo , GPT-4 .. 😛
This was a really, really good overview of their offerings! Having a no-code crash course is perfect for people wanting to understand what exactly HF do.
Thank you Ninja duck. Appreciate your response. Do you have any suggestions for more such content ?
I absolutely loved this video. It was just what I needed. I especially liked that at the beginning, you said that this video was not hands-on coding because that's what I was looking for. I was watching while cooking, so I essentially needed a podcast, and this really nailed it
I'm so glad! When I make some decisions I'm quite skeptical, comments like this help me validate those. Thanks for sharing
@@1littlecoder absolutely! it appears to me that you're making good decisions.. I trust your instinct 🙂
@@grantsmith3653 Thanks Grant!
Took me 8 hours of researching before RUclips recommended this to me... It was worth it!!! Excellent 👌
Awesome! Thank you!
I must say this is the one of the best video i have seen for hugging face, a complete package nothing else is needed. Good Work...appreciate that.
Thank you so much!
I am very happy that they expand their services all the time :)
Happy too!
Really good high level summary without getting too technical. Great job - Would love to see an updated view since this space is moving so fast.
This is what I wanted. All services of Hugging Face are defined in an easy way. I liked your video.
Glad it was helpful!
I love using Huggingface for over two years.
That's great to hear 😊 Maybe this course is well known subject for you
Thank you so much dude this was exactly what I wanted. I have been looking for something like this for ages
Thank you so much! I was kinda lost on navigating HuggingFace, but this video helped me understand HuggingFace more.
I'm planning to make a newer version, Thanks it was helpful!
Thank you for, not only this piece of content, but all content you have created and will create! You always nail it.👊
Thank you
Wow, you explained everything so clearly. God bless you man, glad that I found your channel.
Glad it was helpful :)
Really very very very very useful vedio for beginners ❤ really I was going to give up learning Generative AI but bcoz of your vedio I understand conseptually thank dear ❤❤ I was absent when Hugging Face was taught but I clear all things very well 😊 really appreciate ❤
This really did answer that Twitter question 😂 thank you!
Oh that's great to know. I was quite skeptical but glad to know this feedback :)
Thank you for this fantastic overview of the hugging face ecosystem 🔥
I'm glad you found it useful 😊
After 1 year an updated crash course would be great!
Thanks! What would you like to see new?
@@1littlecoder new features, integrations, hot Models, Spaces etc. These days changes happen very quickly and FOMO impacts us :)
It is extremely useful. Please continue educating newbies like me. I complement you for making this easy to understand introductory course. It is well paced and clear. Well done and thanks. Wish you all the success 👏🙏
Glad it was helpful, Thanks for the kind words!
All services of Hugging Face are defined in an easy way
Thank you very much!
A very good overview! An excellent crash course for beginners.
Thanks I'm planning to make a newer version of this. So if you have any suggestions. Happy to hear.
thanks for this video.. this is exactly what I was searching for.... good job explaining
Glad you liked it
great video. very well structured. thanks for the info!
Glad you enjoyed it!
You have a nice calmness in your voice
Thank you
That was really informative! Thanks for putting this.
Glad you found it helpful. Thanks!
that video was amazing thank you for your work
Excellent
Thank you
Nice, and very helpful! Thanks for making that video.
You're welcome
Thanks brother! Very useful!
My pleasure bro!
Thanks for sharing
Thanks for watching!
Bro, you are amazing
Thanks Bro
Excellent presentation, crisp and concise
Thank you so much for this course.
I'm glad you liked it!
@@1littlecoder I’m getting a PC with RTX 3090 to go deeper into stable Diffusion and training different models locally! Would you be able to advise me whether to keep the current windows 10 or reinstall latest Ubuntu ? Thanks
@@ysy69 I'd suggest you to post this question on fastai forum. The reason is there's an active stable diffusion course going on and a lot of people are building their own rig. So might get good suggestions
@@1littlecoder thank you!
This was a very great video and so informative to understand the basic of huggingface
Thank you Mayur
Very informative. Whish I found this a few months ago. Subscribed to your channel so I don't miss anymore. Will you be making an update of this Crash Course?
I'm happy that you liked this. Do you think an update will help?
@@1littlecoder The hugging face website organisation has slightly changed so updated video might be good. Definitely should help your channel to bring in new viewers.
@@TynOng Thanks for the suggestion 🙏🏽
Thanks Thalaiva!!!
Very good overview. Thanks for putting this together.
Glad you liked it
Very very nice and still very relevant
Thank you!
excellent course.
I’m happy with R Studio 😂
Sir if you can add a video for fine tuning transformer models for various tasks
hello could the generated text go directly into a Google fit seems like I have to copy-paste, could I do that with the api?
THANKS
Great Job. Thanks for sharing.
Thanks for watching!
thank you, great works
Thank you
Thank you it was useful
Thank you for the feedback 😊
awesome information, thanks.
very helpfull thanks
great video!!! Thanks :)
Pls make a nlp project with hugging face transfer learning approach
I've a Hugging Face playlist with a lot of such projects, Could you please check that and let me know your thoughts
Great video @1littlecoder, thank you for doing this, it helped me understand what exactly huggingface is, the products and the problems they solve in just under 40 minutes!
I'm glad you liked it !
great
@@chrisder1814 thanks
@@1littlecodercould you help me understand how rag works?
How do you know what all the models do on hugging face? When most have no descriptions
thanks mate
Thanks Mohammed
Assalamualaikum Abdul, very useful information. What is your advice to someone who is just starting 1st Year AI Engg. Student in India.
we want a updated version of hugging face crash course
How about copyrights if one was to use if for social media content? Any infringements to AI and LLMs?
Could you please elaborate?
@@1littlecoder Lets say i use hugging face to create storytelling content, and post on my youtube channel, is there any possible violation of property rights since the work is generated through artificial intelligence and language learning models...
Why did huggingface named their libraries as "Transformer" which is same as Neural network architecture "Transformer" . It is so confusing for beginners
5/11/2023: now the team members are 173, and they have $160M in total funding.
thanks for the vid
Thanks for these numbers. Quite a growth!
🎉🎉
How can I connect with you?
❤
🙏🏽
Given all the advances in the past year, does this need an update?
Ah, created 1 year ago. That explains the gap 🙂
Still useful video, but I don't see any instance where we would use GOFAI models created by random people in favor of GPT-3.5-Turbo , GPT-4 .. 😛
Great info but very fast
Thank you for the feedback on speed. I'll improve.
a subtitle will be very helpful
Thanks. Will consider it in the future.
Please add captions. I do not have an ear for your language.
You can use RUclips's in-built CC option
@@1littlecoder they're garbage but thanks for the suggestion
Your acent is fucking kill me
You don't have to watch the video 🙏🏽
"lower carbon footprint"... what nonsense.
Hello, do you know how a rag works? Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model