Об этом курсе
4.8
8,984 ratings
1,050 reviews
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. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization. deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content....
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Intermediate Level

Промежуточный уровень

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Предполагаемая нагрузка: 9 hours/week

Прибл. 16 ч. на завершение
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English

Субтитры: English, Chinese (Simplified)

Приобретаемые навыки

Recurrent Neural NetworkArtificial Neural NetworkDeep LearningLong Short-Term Memory (ISTM)
Globe

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.
Calendar

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Intermediate Level

Промежуточный уровень

Clock

Предполагаемая нагрузка: 9 hours/week

Прибл. 16 ч. на завершение
Comment Dots

English

Субтитры: English, Chinese (Simplified)

Программа курса: что вы изучите

1

Раздел
Clock
6 ч. на завершение

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....
Reading
12 видео (всего 112 мин.), 4 тестов
Video12 видео
Notation9мин
Recurrent Neural Network Model16мин
Backpropagation through time6мин
Different types of RNNs9мин
Language model and sequence generation12мин
Sampling novel sequences8мин
Vanishing gradients with RNNs6мин
Gated Recurrent Unit (GRU)17мин
Long Short Term Memory (LSTM)9мин
Bidirectional RNN8мин
Deep RNNs5мин
Quiz1 практическое упражнение
Recurrent Neural Networks20мин

2

Раздел
Clock
4 ч. на завершение

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....
Reading
10 видео (всего 102 мин.), 3 тестов
Video10 видео
Using word embeddings9мин
Properties of word embeddings11мин
Embedding matrix5мин
Learning word embeddings10мин
Word2Vec12мин
Negative Sampling11мин
GloVe word vectors11мин
Sentiment Classification7мин
Debiasing word embeddings11мин
Quiz1 практическое упражнение
Natural Language Processing & Word Embeddings20мин

3

Раздел
Clock
5 ч. на завершение

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....
Reading
11 видео (всего 103 мин.), 3 тестов
Video11 видео
Picking the most likely sentence8мин
Beam Search11мин
Refinements to Beam Search11мин
Error analysis in beam search9мин
Bleu Score (optional)16мин
Attention Model Intuition9мин
Attention Model12мин
Speech recognition8мин
Trigger Word Detection5мин
Conclusion and thank you2мин
Quiz1 практическое упражнение
Sequence models & Attention mechanism20мин
4.8
Direction Signs

44%

начал новую карьеру, пройдя эти курсы
Briefcase

83%

получил значимые преимущества в карьере благодаря этому курсу

Лучшие рецензии

автор: WKMar 14th 2018

I was really happy because I could learn deep learning from Andrew Ng.\n\nThe lectures were fantastic and amazing.\n\nI was able to catch really important concepts of sequence models.\n\nThanks a lot!

автор: SBFeb 19th 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!

Преподавателя

Andrew Ng

Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain

Head Teaching Assistant - Kian Katanforoosh

Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec

Teaching Assistant - Younes Bensouda Mourri

Mathematical & Computational Sciences, Stanford University, deeplearning.ai

О deeplearning.ai

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

О специализации ''Deep Learning'

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....
Deep Learning

Часто задаваемые вопросы

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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