Этот курс входит в специализацию ''Специализация Deep Learning'

Sequence Models

от партнера

deeplearning.ai

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....

Course 5 of 5 in the

специализации ''Deep Learning''

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Промежуточный уровень

Предполагаемая нагрузка: 9 hours/week

Прибл. 16 ч. на завершение

English

Субтитры: English, Chinese (Simplified)

Приобретаемые навыки

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

Course 5 of 5 in the

специализации ''Deep Learning''

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Промежуточный уровень

Предполагаемая нагрузка: 9 hours/week

Прибл. 16 ч. на завершение

English

Субтитры: English, Chinese (Simplified)

Программа курса: что вы изучите

1

Раздел

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....

12 видео (всего 112 мин.), 4 тестов

12 видео

Why sequence models2мин

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

1 практическое упражнение

Recurrent Neural Networks20мин

2

Раздел

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....

10 видео (всего 102 мин.), 3 тестов

10 видео

Word Representation10мин

Using word embeddings9мин

Properties of word embeddings11мин

Embedding matrix5мин

Learning word embeddings10мин

Word2Vec12мин

Negative Sampling11мин

GloVe word vectors11мин

Sentiment Classification7мин

Debiasing word embeddings11мин

1 практическое упражнение

Natural Language Processing & Word Embeddings20мин

3

Раздел

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....

11 видео (всего 103 мин.), 3 тестов

11 видео

Basic Models6мин

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

1 практическое упражнение

Sequence models & Attention mechanism20мин

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....

Часто задаваемые вопросы

When will I have access to the lectures and assignments?
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.
What will I get if I subscribe to this Specialization?
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.
What is the refund policy?
Is financial aid available?

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Sequence Models

Course 5 of 5 in the

Только онлайн-курсы

Гибкие сроки

Промежуточный уровень

Предполагаемая нагрузка: 9 hours/week

English

Приобретаемые навыки

Course 5 of 5 in the

Только онлайн-курсы

Гибкие сроки

Промежуточный уровень

Предполагаемая нагрузка: 9 hours/week

English

Программа курса: что вы изучите

1

Recurrent Neural Networks

2

Natural Language Processing & Word Embeddings

3

Sequence models & Attention mechanism

44%

83%

Лучшие рецензии

Преподавателя

Andrew Ng

Head Teaching Assistant - Kian Katanforoosh

Teaching Assistant - Younes Bensouda Mourri

О deeplearning.ai

О специализации ''Deep Learning'

Часто задаваемые вопросы