GPT is the first of the papers which proved the effectiveness of unsupervised pre-training for language processing tasks. This video is about GPT-1 which became quite an impactful work in the series of GPT papers that we now have (GPT-2 and GPT-3).


Paper: https://www.cs.ubc.ca/~amuham0....1/LING530/papers/rad
code: https://github.com/openai/finetune-transformer-l
Official OpenAI blog: https://openai.com/blog/language-unsupervised/


Paper Abstract:
Natural language understanding comprises a wide range of diverse tasks suchas textual entailment, question answering, semantic similarity assessment, anddocument classification. Although large unlabeled text corpora are abundant,labeled data for learning these specific tasks is scarce, making it challenging fordiscriminatively trained models to perform adequately. We demonstrate that largegains on these tasks can be realized bygenerative pre-trainingof a language modelon a diverse corpus of unlabeled text, followed bydiscriminative fine-tuningon eachspecific task. In contrast to previous approaches, we make use of task-aware inputtransformations during fine-tuning to achieve effective transfer while requiringminimal changes to the model architecture. We demonstrate the effectiveness ofour approach on a wide range of benchmarks for natural language understanding.Our general task-agnostic model outperforms discriminatively trained models thatuse architectures specifically crafted for each task, significantly improving upon thestate of the art in 9 out of the 12 tasks studied. For instance, we achieve absoluteimprovements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% onquestion answering (RACE), and 1.5% on textual entailment (MultiNLI).


AI Bites
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Twitter: https://twitter.com/ai_bites
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github: https://github.com/ai-bites

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Career Masterclass: How to Get Job-Ready for a DevOps Career With Caltech CTME is a live and interactive webinar hosted by simplilearn in collaboration with real-time industry experts explaining the fundamentals of DevOps and skills required to make a career in DevOps. They will talk about the roles and responsibilities that come with a DevOps career and much more. The host will also cover the course details offered by simplilearn for a better learning experience.

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This Complete Social Media Marketing Full Course 2022 will take you through several important topics to help you make the most out of your social media marketing campaigns. In this Social Media Marketing course for beginners, we will focus on how you can start with social media marketing, Facebook ads, some tips and strategies to advertise on Facebook, how you can rank #1 on YouTube, how you can increase YouTube subscribers, how you can increase Twitter and Instagram followers and some of the best tools you can use for Social Media Marketing. Now, let's have a look at Social Media Marketing and the different avenues it provides to advertise your product or service.

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Learn how to build and deploy a SaaS using NextJS 13.4, DrizzleORM, OpenAI, Stripe, TypeScript, Tailwind, and Vercel. You will gain expertise in the following areas:
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Resources
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🔗 Timestamp 3:20:00 /api/chat/route.ts file https://pastebin.com/crLipeHK

Course developed by @elliottchong

⭐️ Contents ⭐️
⌨️ (0:00:00) Intro & Demo
⌨️ (0:09:53) Set Up NextJS
⌨️ (0:11:59) Set Up Shadcn
⌨️ (0:15:45) Set Up ClerkAuth
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Ламинарное течение, безусловно, красивый феномен. Обязательно поищите ролики на YouTube, если вы его ещё не видели. Один из больших поклонников этого явления — Дестин Сэндлин с канала Smarter Every Day, но Дерек Маллер с канала Veritasium не разделяет его восторженных взглядов и готов доказать, что турбулентное течение круче.

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Перевод: Алексей Лоскутов
Редактура: Алексей Малов
Научная редактура: Леонид Вокин
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© https://www.youtube.com/user/1veritasium
Источник: https://youtu.be/5zI9sG3pjVU

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Можно ли одной рукой поднять над головой гриф с диском весом 20 кг на конце? Если вы невероятно сильный, то можно. А если вы Дерек Маллер, то этот диск придется сначала очень быстро раскрутить.

Ответ: https://youtu.be/FasivFKE0Tc
Гироскопическая прецессия: https://youtu.be/uMQ85tOPSO4

Перевод: Алексей Лоскутов
Редактура: Алексей Малов
Научная редактура: Леонид Вокин
Озвучка: Дмитрий Чепусов, Юрий Катарманов
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© https://www.youtube.com/user/1veritasium
Источник: https://youtu.be/GeyDf4ooPdo

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Deep Generative Modeling
Lecturer: Ava Amini
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Lecture Outline
0:00​ - Introduction
6:10- Why care about generative models?
8:16​ - Latent variable models
10:50​ - Autoencoders
17:02​ - Variational autoencoders
23:25 - Priors on the latent distribution
32:31​ - Reparameterization trick
34:36​ - Latent perturbation and disentanglement
37:40 - Debiasing with VAEs
39:37​ - Generative adversarial networks
42:09​ - Intuitions behind GANs
44:57 - Training GANs
48:28 - GANs: Recent advances
50:57 - CycleGAN of unpaired translation
55:03 - Diffusion Model sneak peak

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