In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.
Este curso forma parte de Programa especializado: Aprendizaje profundo
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Programa especializado: Aprendizaje profundodeeplearning.aiAcerca de este Curso
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Fechas límite flexibles
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Certificado para compartir
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100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 5 de 5 en
Nivel intermedio
Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML
Aprox. 42 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Coreano, Árabe (Arabic)
Habilidades que obtendrás
- Natural Language Processing
- Long Short Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Recurrent Neural Network
- Attention Models
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 5 de 5 en
Nivel intermedio
Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML
Aprox. 42 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Coreano, Árabe (Arabic)
ofrecido por
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Programa - Qué aprenderás en este curso
14 horas para completar
Recurrent Neural Networks
Discover recurrent neural networks, a type of model that performs extremely well on temporal data, and several of its variants, including LSTMs, GRUs and Bidirectional RNNs,
14 horas para completar
12 videos (Total 112 minutos), 4 lecturas, 6 cuestionarios
9 horas para completar
Natural Language Processing & Word Embeddings
Natural language processing with deep learning is a powerful combination. Using word vector representations and embedding layers, train recurrent neural networks with outstanding performance across a wide variety of applications, including sentiment analysis, named entity recognition and neural machine translation.
9 horas para completar
10 videos (Total 99 minutos), 1 lectura, 4 cuestionarios
10 horas para completar
Sequence Models & Attention Mechanism
Augment your sequence models using an attention mechanism, an algorithm that helps your model decide where to focus its attention given a sequence of inputs. Then, explore speech recognition and how to deal with audio data.
10 horas para completar
10 videos (Total 98 minutos), 3 lecturas, 4 cuestionarios
9 horas para completar
Transformer Network
9 horas para completar
5 videos (Total 42 minutos), 4 lecturas, 3 cuestionarios
Reseñas
- 5 stars83,60 %
- 4 stars13,05 %
- 3 stars2,57 %
- 2 stars0,47 %
- 1 star0,28 %
Principales reseñas sobre MODELOS DE SECUENCIAS
por SS1 de ene. de 2020
Learnt a lot about new concepts in RNN and LSTM. Really wanted to learn about these models. This course helped a lot. Everything was new and so fascinating. Loved this course and our teach Andrew NG.
por JR25 de may. de 2019
I am so grateful that Andrew and the team provided such good course, I learn so much from this course, I am so excited that see the wake word detection model actually work in the programming exercise
por AM4 de jul. de 2018
Excellent course! This course extensively covers all of the relevant areas of NLP with a strong practical element allowing you to applying Deep Learning for Sequence Models in real-world scenarios.
por SD27 de sep. de 2018
Great hands on instruction on how RNNs work and how they are used to solve real problems. It was particularly useful to use Conv1D, Bidirectional and Attention layers into RNNs and see how they work.
Acerca de Programa especializado: Aprendizaje profundo
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.
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