Acerca de este Curso

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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)

Qué aprenderás

  • Create word embeddings, then train a neural network on them to perform sentiment analysis of tweets

  • Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model

  • Train a recurrent neural network to extract important information from text, using named entity recognition (NER) and LSTMs with linear layers

  • Use a Siamese network to compare questions in a text and identify duplicates: questions that are worded differently but have the same meaning

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)

ofrecido por

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deeplearning.ai

Programa - Qué aprenderás en este curso

Calificación del contenidoThumbs Up93%(1,537 calificaciones)Info
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

Ver todas las reseñas

Acerca de Programa especializado: Procesamiento de lenguajes naturales

Procesamiento de lenguajes naturales

Preguntas Frecuentes

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