Повышение эффективности глубоких нейросетей from Coursera

Этот курс научит вас «магии» повышения эффективности глубокого обучения. Вы изучите сложный механизм работы глубокого обучения, узнаете, какие параметры влияют на его эффективность и сможете систематически получать хорошие результаты. Также вы изучите TensorFlow.

По прошествии трех недель вы:

— освоите передовые методы создания приложений для глубокого обучения;

— научитесь эффективно использовать распространенные «хитрости» работы с нейросетями, включая инициализацию, L2-регуляризацию и регуляризацию методом исключения, пакетную нормализацию и проверку градиента;

— научитесь выполнять и применять различные алгоритмы оптимизации, такие как мини-пакетный градиентный спуск, моменты, RMSprop и Adam, а также проверять их сходимость;

— освоите передовые методы составления наборов данных для обучения, разработки и тестирования, а также анализа предвзятости и отклонений;

— сможете реализовывать нейронную сеть в TensorFlow.

Это второй курс специализации «Глубокое обучение».

What's inside

Syllabus

Практические аспекты глубокого обучения

Алгоритмы оптимизации

Оптимизация гиперпараметров, пакетная нормализация и программные фреймворки

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Отточено для профессиональных разработчиков глубокого обучения, желающих улучшить производительность своих моделей

Опирается на прочную репутацию Эндрю Нга, одного из ведущих мировых исследователей и преподавателей в области глубокого обучения

Знакомит с методами оптимизации, которые являются важнейшими концепциями в реализации глубокого обучения

Изучает программный фреймворк TensorFlow, популярный в индустрии для реализации глубокого обучения

Оптимизирует модели глубокого обучения, используя передовые методы, такие как пакетная нормализация и выпадение

Требует предварительных знаний в области глубокого обучения, что может быть барьером для новичков

Activities

Coming soon We're preparing activities for Повышение эффективности глубоких нейросетей. These are activities you can do either before, during, or after a course.

Career center

Learners who complete Повышение эффективности глубоких нейросетей will develop knowledge and skills that may be useful to these careers:

Deep Learning Engineer

Deep Learning Engineers design, develop, and implement deep learning models and algorithms for various applications in sectors like computer vision, natural language processing, and speech recognition. This course can be a valuable asset for Deep Learning Engineers as it delves into the practical aspects of deep learning, covering topics such as optimization algorithms, hyperparameter optimization, and package normalization. The course also teaches how to implement neural networks in TensorFlow, which is a popular deep learning framework.

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Machine Learning Engineer

Machine Learning Engineers design and develop machine learning models and algorithms for various applications, including predictive analytics, fraud detection, and image recognition. This course can be beneficial for Machine Learning Engineers as it provides a deep understanding of the underlying mechanisms of deep learning and how to optimize its performance. By gaining expertise in techniques like L2 regularization and batch normalization, Machine Learning Engineers can improve the accuracy and efficiency of their models.

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Data Scientist

Data Scientists analyze and interpret data to extract insights and knowledge. This course can be highly valuable for Data Scientists who wish to specialize in deep learning. It covers advanced methods for building deep learning applications, such as initialization, L2-regularization, and exclusion regularization. By understanding how to optimize and evaluate deep learning models, Data Scientists can make more accurate predictions and solve complex problems.

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Artificial Intelligence Researcher

Artificial Intelligence Researchers explore and develop new techniques and algorithms for artificial intelligence. This course can be beneficial for AI Researchers who want to focus on deep learning. It provides a comprehensive overview of practical deep learning techniques, including optimization algorithms and hyperparameter optimization. By learning how to improve the efficiency and accuracy of deep learning models, AI Researchers can contribute to the advancement of artificial intelligence.

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Computer Vision Engineer

Computer Vision Engineers develop and implement computer vision systems for various applications, such as object detection, facial recognition, and medical imaging. This course can be useful for Computer Vision Engineers who want to gain expertise in deep learning techniques for computer vision. By learning about topics like batch normalization and gradient checking, Computer Vision Engineers can improve the performance and accuracy of their computer vision models.

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Natural Language Processing Engineer

Natural Language Processing Engineers develop and implement natural language processing systems for various applications, including machine translation, text classification, and question answering. This course can be useful for NLP Engineers who want to specialize in deep learning for NLP. It covers advanced techniques for optimizing deep learning models, such as RMSprop and Adam, which can help NLP Engineers improve the accuracy and efficiency of their NLP systems.

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Speech Recognition Engineer

Speech Recognition Engineers develop and implement speech recognition systems for various applications, including voice control, transcription, and language identification. This course may be beneficial for Speech Recognition Engineers who want to gain expertise in deep learning techniques for speech recognition. By understanding how to optimize and evaluate deep learning models, Speech Recognition Engineers can improve the accuracy and efficiency of their speech recognition systems.

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Robotics Engineer

Robotics Engineers design, develop, and implement robots for various applications, such as manufacturing, healthcare, and space exploration. This course may be useful for Robotics Engineers who want to incorporate deep learning into their robotics systems. By learning about topics like optimization algorithms and hyperparameter optimization, Robotics Engineers can improve the performance and autonomy of their robots.

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Software Engineer

Software Engineers design, develop, and implement software applications for various industries and domains. This course may be useful for Software Engineers who want to specialize in deep learning. By gaining expertise in techniques like L2-regularization and batch normalization, Software Engineers can develop more efficient and accurate software applications that leverage deep learning.

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Data Analyst

Data Analysts collect, analyze, and interpret data to extract insights and knowledge. This course may be useful for Data Analysts who want to gain expertise in deep learning techniques for data analysis. By understanding how to optimize and evaluate deep learning models, Data Analysts can improve the accuracy and efficiency of their data analysis pipelines.

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Business Intelligence Analyst

Business Intelligence Analysts analyze and interpret data to provide insights and recommendations for businesses. This course may be useful for Business Intelligence Analysts who want to gain expertise in deep learning techniques for business intelligence. By understanding how to optimize and evaluate deep learning models, Business Intelligence Analysts can improve the accuracy and efficiency of their data analysis and provide more valuable insights to businesses.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze and predict financial markets. This course may be useful for Quantitative Analysts who want to gain expertise in deep learning techniques for quantitative analysis. By understanding how to optimize and evaluate deep learning models, Quantitative Analysts can improve the accuracy and efficiency of their financial models.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex problems in various industries. This course may be useful for Operations Research Analysts who want to gain expertise in deep learning techniques for operations research. By understanding how to optimize and evaluate deep learning models, Operations Research Analysts can improve the efficiency and effectiveness of their solutions.

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Financial Analyst

Financial Analysts analyze and interpret financial data to provide insights and recommendations for investors and businesses. This course may be useful for Financial Analysts who want to gain expertise in deep learning techniques for financial analysis. By understanding how to optimize and evaluate deep learning models, Financial Analysts can improve the accuracy and efficiency of their financial analysis and provide more valuable insights to clients.

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Actuary

Actuaries use mathematical and statistical techniques to assess and manage risk. This course may be useful for Actuaries who want to gain expertise in deep learning techniques for actuarial science. By understanding how to optimize and evaluate deep learning models, Actuaries can improve the accuracy and efficiency of their risk assessments and provide more valuable insights to clients.

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