Acerca de este Curso

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Fechas límite flexibles
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Nivel intermedio

We recommend that you have taken the first two courses of the Natural Language Processing Specialization, offered by deeplearning.ai

Aprox. 19 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Habilidades que obtendrás

Word EmbeddingSentiment with Neural NetsSiamese NetworksNatural Language GenerationNamed-Entity Recognition
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio

We recommend that you have taken the first two courses of the Natural Language Processing Specialization, offered by deeplearning.ai

Aprox. 19 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

ofrecido por

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deeplearning.ai

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

5 horas para completar

Neural Networks for Sentiment Analysis

5 horas para completar
9 videos (Total 35 minutos), 3 lecturas, 1 cuestionario
9 videos
Neural Networks for Sentiment Analysis3m
Trax: Neural Networks2m
Why we recommend Trax13m
Trax: Layers3m
Dense and ReLU Layers1m
Serial Layer1m
Other Layers 3m
Training2m
3 lecturas
Connect with your mentors and fellow learners on Slack!10m
Reading: (Optional) Trax and JAX, docs and code15m
How to Refresh your Workspace10m
Semana
2

Semana 2

5 horas para completar

Recurrent Neural Networks for Language Modeling

5 horas para completar
8 videos (Total 27 minutos)
8 videos
Recurrent Neural Networks4m
Applications of RNNs3m
Math in Simple RNNs3m
Cost Function for RNNs1m
Implementation Note 2m
Gated Recurrent Units4m
Deep and Bi-directional RNNs 3m
Semana
3

Semana 3

4 horas para completar

LSTMs and Named Entity Recognition

4 horas para completar
6 videos (Total 24 minutos), 3 lecturas, 1 cuestionario
6 videos
Introduction to LSTMs4m
LSTM Architecture3m
Introduction to Named Entity Recognition3m
Training NERs: Data Processing 4m
Computing Accuracy1m
3 lecturas
(Optional) Intro to optimization in deep learning: Gradient Descent10m
(Optional) Understanding LSTMs10m
Long Short-Term Memory (Deep Learning Specialization C5)10m
Semana
4

Semana 4

5 horas para completar

Siamese Networks

5 horas para completar
8 videos (Total 33 minutos), 1 lectura, 1 cuestionario
8 videos
Architecture3m
Cost Function3m
Triplets6m
Computing The Cost I5m
Computing The Cost II6m
One Shot Learning2m
Training / Testing3m
1 lectura
Acknowledgments10m

Reseñas

Principales reseñas sobre NATURAL LANGUAGE PROCESSING WITH SEQUENCE MODELS

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Acerca de Programa especializado: Procesamiento de lenguajes naturales

Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper....
Procesamiento de lenguajes naturales

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