Pluralsight.Natural.Language.Processing.with.PyTorch-XQZT
þ Þ
ß Û þ
ßÜÜ ²
Another °ÛÛÛ°
²ÛÛ ° °°° °°° ß
ÛÛ² ²²² ²²² Ü
²ÛÛ° ÛÛÛ ÛÛÛ °
ßß ÛÛÛ ÛÛÛ ²ÛÛÛ Ü°°
ßßÜÜ °ÛÛÛ °ÛÛ²Ý ° ÛÛÛÜ ÜÜÛ²±
ßßܲÛÛ ²ÛÛ°² ÜÝ ÛÛÛÜÜÛÛ²ÛÛ²
ÛÛÛßÜÜ° ÛÛÛ ±Ü ÝÞÛÜÜÛÛÛÛÛÛÜ°ÛÛÛ
ÛÛÛ ²ßßÜÜÜÛÛÛ° ß²ÛÛ²ÜÜÜÛ Ûßßß ßÛÛÛÛÛÛÛ ÜÜÜÜ
ÛÛÛ ÛÛÛßßßÜÜÛÛÛÛÛ² Ü ²ÛÛÛÛÛÛ²ÜÜ ° ÜÜÛÛÛÛÛÛ
ÛÛÛ ° ÜÜÜÛÛÛ²ßßßß °²ÛÛÛÝ ² ° ²ÛÛ²ßÛ²±°ÛÛÛÜܲ ÜÜÜÛÛÛÛÛßßß
ÛÛÛÜÜß²ß ÛÛÛ ²ÛÛÛ ÞÝ ÛÛÛÛ° ßß²ÛÛÛÛÛÛÛÛÛÛÛÛÛ²ßß þ
ÜÜßß²ÛÛ ° ÛÛÛ ÛÛÛÛ ÛÛÛÛÛÛ ß²ÛÛÛÛßßß
ÜÜ °ÛÛ ° ÛÛÛ ß ÛÛÛÛ ÛÛÛ² ÛÛÛ ÛÛÛÛ Ü
ÛÛÛ °ÛÛÛ ÜÜÛ°²ÜÜ ÛÛÛÛ ° ÛÛÛÛ ÛÛÛ ²ÛÛÛ ÜÜ
Ü ²ÛÛ °ÛÛ² ÜÛÛß ßÛÛÛÜ ²²²² °ÛÛÛÛ ÛÛÛ ²ÛÛÛ ÜÜ
ÛÛ²ÛÛ² ²ÛÛ ÛÛÛ² °°°° ²ÛÛÛ ²ÛÛÛ ±ÛÛÛ ÜÜßß
°ÛÛÛÛ° ²ÛÛ ²ÛÛ² ÛÛÛÛ ÛÛÛ ²²² °ÛÛÛÛ ßß
²ÛÛÛÛ °ÛÛ² ÛÛ °ÛÛÛ ÛÛÛÛ ÛÛÛ °°° ° Û²² ßß
ÛÛÛ ÛÛ² ÛÛÛ Û° ÛÛÛ ÛÛÛÛ ÛÛÛ þ ° °°ÜÜ
ÛÛÛ ÛÛ² ÛÛÛ °°± ÛÛ² ÛÛÛÛ² ßßÛÛ² ° ÜÜß ÜÜÛÛÛ
ÛÛÛ ²ÛÛ ÛÛ² ±² ²ÛÛ ÛÛÛÛ ± ß Ü²ßß ßß ÛÛÛÛ
²ÛÛ °ÛÛÛ ÛÛÛÜ °²ÛÛÛÛ ÛÛÛÛ ° ÜÜßßÛÛÛßÜÜ ÛÛÛ
°ÛÛÛ ° ÛÛÛÜ ßßÛÛÛÛ±ÛÛÛ ÛÛÛÛÛÛܲÜÜÜÜÜÜÜÜÛÛ²Ü ßßÛÜÜ° ÛÛÛÛ
ÛÛÛ° ÜÜÜÜ° ßÛÛÛÜ ßßß²ÛÛ°ÛÛÛÛ²ßßß± °ßßß²Û²±ÛÜ ²ßßÜÜÛÛÛÜÜÜ
ÛÛÛ ÛÛßßßß²ÛÛÛÛÛÛÛÛÜÜ °ÛÛÛßß ° ° ßß°Û²Ü ÛÛÛ ßßßßÛÜÜÜÜ
²ÛÛ ° ßßÛÛÛÛÛ ÛÛ ° ßß ° ÜÛÛÛÛßßßßßßß þ
°ÛÛÛ ° ßßß ÛÛ ÜÜܲßßß ÛÛÛ
ÛÛÛ ÛÛ ÛÛ ÜÜßßß ° ÛÛÛ
ÛÛÛ Ü ÛÛ ± ° Ü ° ÛÛÛ
²²² ÛÛ ° ß Ü ° ßß
°°° þ ²² ß ß ÜÜ
°° ß þ þ
Ü Ü
Üß release
Ü
Natural Language Processing with PyTorch
374.3 MB
08/06/2019
Course # : N/A
Published : Aug 5 2019
Modified : N/A
URL : www.pluralsight.com/library/courses/natural-language-processing-pytorch
Author : Janani Ravi
Duration : 2h 58m
Skill : Advanced
Exer/Code : [X]
Description:
From chatbots to machine-generated literature, some of the hottest applications
of ML and AI these days are for data in textual form. In this course, Natural
Language Processing with PyTorch, you will gain the ability to design and
implement complex text processing models using PyTorch, which is fast emerging
as a popular choice for building deep-learning models owing to its flexibility,
ease-of-use, and built-in support for optimized hardware such as GPUs. First,
you will learn how to leverage recurrent neural networks (RNNs) to capture
sequential relationships within text data. Next, you will discover how to
express text using word vector embeddings, a sophisticated form of encoding that
is supported by out-of-the-box in PyTorch via the torchtext utility. Finally,
you will explore how to build complex multi-level RNNs and bidirectional RNNs to
capture both backward and forward relationships within data. You will round out
the course by building sequence-to-sequence RNNs for language translation. When
you are finished with this course, you will have the skills and knowledge to
design and implement complex natural language processing models using
sophisticated recurrent neural networks in PyTorch.