Последовательные модели from Coursera

Данный курс научит вас строить модели естественных языков, звуков и других последовательных данных. Благодаря глубокому обучению последовательные алгоритмы сегодня работают в разы лучше, чем ещё два года назад. Это открывает широчайший спектр возможностей применения алгоритмов в распознавании речи, синтезе музыки, чат-ботах, машинном переводе, понимании естественных языков и во многом другом.

Вы научитесь:

— строить и обучать рекуррентные нейронные сети (РНС, RNN), а также широко используемые управляемые рекуррентные блоки (УРБ, GRU) и долгую краткосрочную память (ДКП, LSTM);

— применять последовательные модели в задачах по обработке естественного языка, включая синтез текста;

— применять модели последовательностей к звуковой информации, например для распознавания речи или синтеза музыки.

Это пятый и заключительный курс специализации «Глубокое обучение».

Задача по программированию машинного перевода с глубоким обучением, содержащаяся в этом курсе, разработана deeplearning.ai совместно с партнером — Институтом глубокого обучения NVIDIA (DLI). У вас будет возможность создать проект по глубокому обучению с современным, актуальным для индустрии содержанием.

What's inside

Syllabus

Рекуррентные нейронные сети

В этом разделе вы познакомитесь с принципами работы рекуррентных нейронных сетей (РНС, RNN). Этот тип сетей показывает прекрасную работу с темпоральными данными и существует в нескольких вариантах, таких как LSTM (ДКП), GRU (УРБ), и двунаправленная РНС (Bidirectional RNN), о которых вы узнаете в этом разделе.

Обработка естественного языка и векторное представление слов

Сочетание обработки естественного языка и глубокого обучения — очень важное сочетание. Используя векторное представление слов и слои встраивания, вы сможете обучать рекуррентные нейронные сети, добиваясь выдающейся производительности в широком спектре областей. Примеры применения: анализ тональности текста, распознавание именованных сущностей и машинный перевод.

Последовательные модели и механизм внимания

Последовательные модели могут быть дополнены с использованием механизма внимания. С помощью этого алгоритма ваша модель сможет понять, на чем следует сосредоточить внимание, с учетом последовательности входных данных. На этой неделе вы также узнаете о распознавании речи и работе с аудиоданными.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches core concepts in natural language processing, such as tokenization, part-of-speech tagging, and named entity recognition, which are essential skills for NLP data scientists

Develops strong foundational skills for a career in machine learning by teaching the theory behind RNNs and relevant advanced modeling techniques

Provides hands-on programming experience with deep learning models in a real-world NLP context

Teaches advanced deep learning models such as LSTMs and GRUs, which are essential for NLP tasks such as sentiment analysis and text classification

Introduces statistical NLP and word embeddings, which provide students with a deeper understanding of how language is represented and processed by machines

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Последовательные модели with these activities:

Чтение книги "Глубокое обучение с использованием Python"

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Рассмотрите основные концепции и приложения глубокого обучения, чтобы освежить свои знания и расширить свое понимание.

View Глубокое обучение на Python. 2-е межд. издание... on Amazon

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Ознакомьтесь с основами глубокого обучения
Изучите передовые техники в области обработки естественного языка

Volunteer as a mentor for beginner Python learners

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Reinforce your understanding of Python by helping others learn the fundamentals, which can also improve your communication skills.

Browse courses on Python Programming

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Find a mentoring platform or program
Connect with beginner Python learners

Review probability and linear algebra

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Review probability and linear algebra concepts in preparation for the course's mathematical foundations.

Browse courses on Probability

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Go over basic probability theory
Brush up on linear algebra fundamentals

Four other activities

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Reinforce your Python skills

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Refresh your Python skills, including advanced data structures, algorithms, and libraries.

Browse courses on Python Programming

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Review core Python concepts
Practice programming challenges

Learn about RNNs and LSTM models

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Complete online tutorials to deepen your understanding of RNNs and LSTM models, the core concepts of this course.

Browse courses on RNN

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Find tutorials on RNNs and LSTM models
Follow the tutorials and work through examples

Practice implementing RNNs from scratch

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Test your understanding of RNNs by implementing them from scratch, which will enhance your problem-solving abilities.

Browse courses on RNN

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Choose a programming language
Implement a simple RNN model
Implement an LSTM model

Build a project that uses RNNs

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Demonstrate your skills by building a project that utilizes RNNs, providing you with practical experience and a portfolio piece.

Browse courses on RNN

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Identify a problem or application
Design and implement an RNN model
Evaluate the performance of your model

Career center

Learners who complete Последовательные модели will develop knowledge and skills that may be useful to these careers:

NLP Engineer

Natural Language Processing Engineers are responsible for developing and maintaining Natural Language Processing (NLP) systems. NLP is a subfield of artificial intelligence concerned with giving computers the ability to understand and generate human language. NLP Engineers use their knowledge of linguistics, computer science, and mathematics to design and implement NLP systems that can be used for a variety of tasks, such as machine translation, text summarization, and spam filtering. The course you mentioned on Sequential Models would be quite helpful in a career as an NLP Engineer, as this course focuses on building models for natural language tasks. The course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building NLP systems.

See salaries and explore the career path for NLP Engineer

Machine Learning Engineer

Machine Learning Engineers design, develop, and maintain machine learning models. They use their knowledge of machine learning algorithms to build models that can learn from data and make predictions. Machine Learning Engineers work in a variety of industries, such as finance, healthcare, and manufacturing. The course you mentioned on Sequential Models is a useful course for Machine Learning Engineers, as it provides a solid foundation in recurrent neural networks and other sequential models. These models are essential for building machine learning systems that can process sequential data, such as text and audio data.

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Data Scientist

Data Scientists use their knowledge of statistics, machine learning, and data analysis to extract insights from data. They work in a variety of industries, such as finance, healthcare, and retail. The course you mentioned on Sequential Models would be helpful for Data Scientists who want to learn more about building models for sequential data. This course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building models that can process sequential data.

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Software Engineer

Software Engineers design, develop, and maintain software systems. They work in a variety of industries, such as finance, healthcare, and manufacturing. The course you mentioned on Sequential Models would be useful for Software Engineers who want to learn more about building models for sequential data. This course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building models that can process sequential data.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

Quantitative Analysts use their knowledge of mathematics, statistics, and computer science to develop and implement financial models. They work in a variety of financial institutions, such as banks, hedge funds, and asset management companies. The course you mentioned on Sequential Models may be useful for Quantitative Analysts who want to learn more about building models for sequential data. This course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building models that can process sequential data.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use their knowledge of mathematics, statistics, and computer science to solve problems in business and industry. They work in a variety of industries, such as manufacturing, transportation, and logistics. The course you mentioned on Sequential Models may be useful for Operations Research Analysts who want to learn more about building models for sequential data. This course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building models that can process sequential data.

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Product Manager

Product Managers are responsible for the development and launch of new products. They work in a variety of industries, such as technology, healthcare, and manufacturing. The course you mentioned on Sequential Models may be useful for Product Managers who want to learn more about building models for sequential data. This course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building models that can process sequential data.

See salaries and explore the career path for Product Manager

Business Analyst

Business Analysts use their knowledge of business processes and data analysis to help organizations improve their performance. They work in a variety of industries, such as finance, healthcare, and manufacturing. The course you mentioned on Sequential Models could be helpful for Business Analysts who want to learn more about building models for sequential data. This course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building models that can process sequential data.

See salaries and explore the career path for Business Analyst

Marketing Manager

Marketing Managers are responsible for developing and implementing marketing campaigns. They work in a variety of industries, such as technology, healthcare, and manufacturing. The course you mentioned on Sequential Models may be useful for Marketing Managers who want to learn more about building models for sequential data. This course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building models that can process sequential data.

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Consultant

Consultants provide advice and guidance to businesses and organizations on a variety of topics, such as strategy, operations, and finance. They work in a variety of industries, such as technology, healthcare, and manufacturing. The course you mentioned on Sequential Models may be useful for Consultants who want to learn more about building models for sequential data. This course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building models that can process sequential data.

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Financial Analyst

Financial Analysts use their knowledge of finance and economics to analyze financial data and make investment recommendations. They work in a variety of financial institutions, such as banks, hedge funds, and asset management companies. The course you mentioned on Sequential Models may be useful for Financial Analysts who want to learn more about building models for sequential data. This course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building models that can process sequential data.

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Teacher

Teachers teach students at all levels, from elementary school to college. They develop lesson plans, teach lessons, and assess student learning. The course you mentioned on Sequential Models may be useful for Teachers who want to learn more about building models for sequential data. This course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building models that can process sequential data. This knowledge could be helpful for Teachers who want to develop educational software or other tools that use sequential data.

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Librarian

Librarians manage and organize libraries and provide reference and research services to patrons. They work in a variety of settings, such as public libraries, school libraries, and academic libraries. The course you mentioned on Sequential Models may be useful for Librarians who want to learn more about building models for sequential data. This course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building models that can process sequential data. This knowledge could be helpful for Librarians who want to develop tools for organizing and searching library collections.

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Museum curator

Museum Curators are responsible for the care and display of museum collections. They work in a variety of settings, such as museums, galleries, and historical sites. The course you mentioned on Sequential Models may be useful for Museum Curators who want to learn more about building models for sequential data. This course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building models that can process sequential data. This knowledge could be helpful for Museum Curators who want to develop tools for organizing and searching museum collections.

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Archivist

Archivists preserve and maintain historical documents and artifacts. They work in a variety of settings, such as museums, libraries, and government agencies. The course you mentioned on Sequential Models may be useful for Archivists who want to learn more about building models for sequential data. This course covers topics such as recurrent neural networks, LSTM, and GRU, all of which are important for building models that can process sequential data. This knowledge could be helpful for Archivists who want to develop tools for organizing and searching historical collections.

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