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Papers With Video
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Добавлен 23 янв 2021
Videos providing intuitive understanding of data science topics.
Stable Diffusion Explained From Scratch: An Intuitive Technical Understanding of AI Image Generators
More than 15 billion images were generated by AI in the last 1.5 years. People are using AI Image generation algorithms to create an average of ~28 million images per day. So in this video, we break down the stable diffusion method, which is at the heart of almost every AI art generation tool out there - Adobe Firefly, MidJourney, DALL-E, and others. Hope you find this informative!
You can find the accompanying notes for this video here - mailchi.mp/833866be8668/ai-image-generation-from-scratch
OUTLINE
00:00 Introduction
00:33 Outline
02:10 Forward Diffusion (Noising) and Backward Diffusion (De-Noising)
06:30 Convolutional Neural Networks (CNNs)
11:05 U-Net
12:40 Residual Networks (ResNets)
14:44...
You can find the accompanying notes for this video here - mailchi.mp/833866be8668/ai-image-generation-from-scratch
OUTLINE
00:00 Introduction
00:33 Outline
02:10 Forward Diffusion (Noising) and Backward Diffusion (De-Noising)
06:30 Convolutional Neural Networks (CNNs)
11:05 U-Net
12:40 Residual Networks (ResNets)
14:44...
Просмотров: 840
Видео
ChatGPT Explained From Scratch: An Intuitive Technical Understanding of AI Chatbots
Просмотров 17 тыс.9 месяцев назад
Discover the technical secrets behind ChatGPT, the record-breaking chatbot that's taking the world by storm! Dive deep into the technical wonders that power this phenomenon without drowning in complex math. Everything you need to know about the fastest-growing app in human history - join the exploration! 🔍 OUTLINE 00:00 Intro & Overview 00:20 Outline 01:16 Symbolic Language Models 80s-90s 02:...
Word2Vec Papers Explained From Scratch: Skip-Gram with Negative Sampling
Просмотров 10 тыс.3 года назад
Today we are going to be taking you guys through the 2 Word2Vec papers! PAPER Paper 1: Efficient Estimation of Word Representations in Vector Space. arxiv.org/abs/1301.3781 Paper 2: Distributed Representations of Words and Phrases and their Compositionality. arxiv.org/abs/1310.4546 OUTLINE 00:00 Intro & Overview 00:34 Word2Vec Paper 1 Abstract 02:32 Feed Forward Neural Network Language Model ...
I think it would help to explain that a RGB image when applied with a 3D filter, still results in a 2D feature map. I got confused when watching that part, and had to look it up online that it doesn't produce a 3D feature map (this is a great visual example: ruclips.net/video/KTB_OFoAQcc/видео.html) But overall, fantastic video! One of the best I have seen explaining how Stable Diffusion works.
Best tutorial availabel on youtube for stable diffusion. I spent my complete day understading this, but did not understood anything, Right now, All clear with your video. Thanks, Keep helping us.
This channel is a hidden gem! Haven’t seen content like this anywhere else, keep making more videos 😄
such perfectly curated content. Amazing job please keep up the good work!!
What an amazing content , very descriptive . Appreciate the effort for sharing complete information!!
Hi I really enjoy your content, well done can you share what software/app you use to draw on the papers like that? And how to do you highlight the text? Thanks
all I want from AI is a voice filter.
Please make more videos
I love you, for making this video
I pressed like button on a video after many months. The content in this video is dense but it tied together the history of language models, and why ChatGpt is designed in the way it is, in that historical context. Loved it!
To the author - nice presentation style, nice diagrams. (Nice as in really good!). But please fill in some more detail or better examples. Address some obvious questions like "why multiple layers?" Fur the transformer click, are all words represented in the output of each block? Things like this
Can you do another video on the leaked information about GPT4, such as Mixture of Experts?
I was using n-grams in LM in the 90's... there is an error in the video: the unigram (1-gram) is the probabilty of the word itself, its frequency in the training set... the bigram (2-gram) is the probabilty of apparition of a word given the previous word... n-gram, probability of a word given the (n-1) previous words... in fact, to limit the size of the tables and to take into account words and combinations absent or unfrequent in the training set, a threshold is applied to exclude all the low probabilities and redistribute that probability mass (to handle dufferently the unfrequent combinations)
Awesome video, please make more
Amazing video. Powerfully insightful.
Thanks for the explanation
I don't know who are you but make more videos like this pls!
Probably the best video i ever watched on this topic, the way you explain is so intuitive and amazing,. thanks for your efforts. keep doing it
Wow this was amazing! I love the video & teaching style I recently purchased a Tablet and I’ve been wanting to do more breakdowns like this. Could you please share what tool/app you use to draw and create the videos?
Hey, did you work out what kind of tablet/software he uses? Thanks
Can you say what environment you use for cresting these tutorials?
You are thè coolest
After watch this, the "AI" feel's less AI, bacause the behavior that it act's is human dependen't; even our self's share same feature, considering the bio,psycho and social model of mind that we have and the big portion that the social are stand, but in the end the human intellectual are very depend on the "freedom" or the "nonconformist" feature that human has If this stuff is just following what human feed to him, and take those information to thons of layer to get outcome, then this is more like a "brute force" action, instead of a intellectual emergence
Well,I might be wrong if the idea or concept of "freedom" or "rebellious" are it self can be human feeded -.-
@@chuangcaiyan7114 welcome to AGI. gpt5 is Q* project. now, AI don't need humans for prompting and datasets. he will create it himself. also able to create new algorithums and solve complex maths without using any human data.
Andrej karpathy - How do you teach a computer? The same way you teach a human. That is the jist of Software 2.0 in the 21st century.
One of best 'How' videos that I have watched recently... ❤
Nice tutorial, but please writting more clear, hard to read the letters.
Great content. Keep up the works.
Excellent content! Do make more such videos! Great for beginners as well!
Yes, It is a great top level view. Now dig down 10 more layers to really understand the mechanics of what is happening.
From your understanding, will LLMs be able to replace programmers? Because all of these seems just applied statistics and not something "unseen".
The role of programmers will change. Research Andraj Karpathy and Software 2.0. He is one of the Gurus behind Deep Machine Learning. The future that he saw in 2016 is now being seen today by the likes of Google and Microsoft - Co-pilots
Incredible video about ChatGPT-explains intricate details in a simple way, easy to grasp, and highly informative. A must-see for a clear understanding of ChatGPT!
dont understand how this video hasnt blown up yet
nice work bro.. keep it up
Need more
what an amazing content, thanks for putting in so much content for developing a clear understanding
great content!!!
What am amazing video to understand GPT's and LLMS. Thanks bro.
Guys I absolutely loved the explanation and kudos to all the efforts u put in to understand the subject so thoroughly to put out such beautiful explanations. Do not get demotivated by the number of likes and subscribers, I see a great potential for this channel. All the best.
Highly underrated channel. Dude you are doing a great job. Thanks for this nice video.
Best video I saw on SGNS thanks sir !
damn there is only one video , I wanted to see more videos
This channel should make more videos.Excellent stuffs btw
Amazing Explanation
at 3:32, shouldn't the first term for Q be V X D instead of N X D?
Great video but could you please slow down in your talking speed? It's insanely fast and sometimes it is hard to understand certain words even if I slow down the video
very good, keep doing the good work, dont be disheartened by less views. Very well explained.
keep up the good work
your contents are really good
Absolutely amazing work
This was a great explanation
What is the name of your robot (text to voice) ?
Amazing explanation!!! Thank you!
I am really really thankful for such a nice explanation. I would request you to not get demotivated from less views or likes. Keep up the good work.