This video explores the GPT-2 paper "Language Models are Unsupervised Multitask Learners". The paper has this title because their experiments show how massive language models trained on massive datasets can perform tasks like Question Answering and Translation by carefully formatting them as language modeling inputs.

Paper Links
GPT-2 Paper: https://cdn.openai.com/better-....language-models/lang
AllenNLP GPT-2 Demo: https://demo.allennlp.org/next....-token-lm?text=Joel%
The Illustrated GPT-2: http://jalammar.github.io/illustrated-gpt2/
Combining GPT2 and BERT to make a fake person: https://www.bonkerfield.org/20....20/02/combining-gpt-

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Movie/album: Main Khiladi Tu Anari

Singers: Alka Yagnik
Song Lyricists: Rahat Indori
Music Composer: Anu Malik
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Music Label: Venus Records
Starring: Akshay Kumar, Saif Ali Khan, Shilpa Shetty, Raageshwari
Release on: 23rd September, 1994


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In the previous tutorial, we covered how to use channels to send and receive values with goroutines. That said, it was just a basic example. In reality, we're likely to have questions of synchronization and iterating through known, or unknown, numbers of channel returns.

Text tutorials and sample code: https://pythonprogramming.net/go/
https://twitter.com/sentdex
https://www.facebook.com/pythonprogramming.net/
https://plus.google.com/+sentdex

Time series forecasting has a wide range of applications: finance, retail, healthcare, IoT, etc. Recently deep learning models such as ESRNN or N-BEATS have proven to have state-of-the-art performance in these tasks. Nixtlats is a python library that we have developed to facilitate the use of these state-of-the-art models to data scientists and developers, so that they can use them in productive environments. Written in pytorch, its design is focused on usability and reproducibility of experiments. For this purpose, nixtlats has several modules:

Data: contains datasets of various time series competencies.
Models: includes state-of-the-art models.
Evaluation: has various loss functions and evaluation metrics.

Objective:

- To introduce attendees to the challenges of time series forecasting with deep learning.
- Commercial applications of time series forecasting.
- Describe nixtlats, their components and best practices for training and deploying state-of-the-art models in production.
- Reproduction of state-of-the-art results using nixtlats from the winning model of the M4 time series competition (ESRNN).

Project repository: https://github.com/Nixtla/nixtlats.

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