This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.
Este curso faz parte do Programa de cursos integrados Aprendizagem profunda
Sequence Models
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Informações sobre o curso
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Habilidades que você terá
Recurrent Neural NetworkArtificial Neural NetworkDeep LearningLong Short-Term Memory (ISTM)
Resultados de carreira do aprendiz
39%
comecei uma nova carreira após concluir estes cursos
40%
consegui um benefício significativo de carreira com este curso
13%
recebi um aumento ou promoção
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 5 de 5 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 19 horas para completar
Inglês
Legendas: Chinês (tradicional), Chinês (simplificado), Coreano, Inglês, Espanhol
Programa - O que você aprenderá com este curso
6 horas para concluir
Recurrent Neural Networks
Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section.
6 horas para concluir
12 vídeos (Total 112 mín.), 2 leituras, 4 testes
12 videos
Notation9min
Recurrent Neural Network Model16min
Backpropagation through time6min
Different types of RNNs9min
Language model and sequence generation12min
Sampling novel sequences8min
Vanishing gradients with RNNs6min
Gated Recurrent Unit (GRU)17min
Long Short Term Memory (LSTM)9min
Bidirectional RNN8min
Deep RNNs5min
2 leituras
Gated Recurrent Unit (GRU) *CORRECTION*1min
Long Short Term Memory (LSTM) *CORRECTION*1min
1 exercício prático
Recurrent Neural Networks30min
4 horas para concluir
Natural Language Processing & Word Embeddings
Natural language processing with deep learning is an important combination. Using word vector representations and embedding layers you can train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment analysis, named entity recognition and machine translation.
4 horas para concluir
10 vídeos (Total 102 mín.), 1 leitura, 3 testes
10 videos
Word Representation10min
Using word embeddings9min
Properties of word embeddings11min
Embedding matrix5min
Learning word embeddings10min
Word2Vec12min
Negative Sampling11min
GloVe word vectors11min
Sentiment Classification7min
Debiasing word embeddings11min
1 leituras
GloVe word vectors *CORRECTION*1min
1 exercício prático
Natural Language Processing & Word Embeddings30min
5 horas para concluir
Sequence models & Attention mechanism
Sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This week, you will also learn about speech recognition and how to deal with audio data.
5 horas para concluir
11 vídeos (Total 103 mín.), 3 leituras, 3 testes
11 videos
Basic Models6min
Picking the most likely sentence8min
Beam Search11min
Refinements to Beam Search11min
Error analysis in beam search9min
Bleu Score (optional)16min
Attention Model Intuition9min
Attention Model12min
Speech recognition8min
Trigger Word Detection5min
Conclusion and thank you2min
3 leituras
Bleu Score *CORRECTION*1min
Corrections10min
Instructions if you are unable to open your notebook10min
1 exercício prático
Sequence models & Attention mechanism30min
Avaliações
Principais avaliações do SEQUENCE MODELS
por PJApr 3, 2019
The previous courses raised the bar and expectations. The assignments for Week 1 and Week 2 were a bit unclear. Lectures for Week 1 and Week 2 can be improved as well. Besides, this is a great course!
por AMJun 30, 2019
The course is very good and has taught me the all the important concepts required to build a sequence model. The assignments are also very neatly and precisely designed for the real world application.
por ACJun 23, 2019
Very well produced and explained. In my case, the nature of the Sequence Model makes understanding the concepts and finishing the assignment more challenging than other segments of the specialization.
por SBFeb 18, 2018
Loved the course - it was very interesting. It is also pretty complex, so will probably go through it again to review the concepts and how the models work. Thank you for this wonderful course series!
por SSJan 1, 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 JRMay 25, 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 SDSep 27, 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.
por NMFeb 20, 2018
Hope can elaborate the backpropagation of RNN much more. BP through time is a bit tricky though we do not need to think about it during implementation using most of existing deep learning frameworks.
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deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
Sobre Programa de cursos integrados Aprendizagem profunda
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.
AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.
We will help you master Deep Learning, understand how to apply it, and build a career in AI.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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