النماذج المتعاقبة from Coursera

ستتعلم في هذا المساق كيفية إنشاء نماذج للغة الطبيعية والصوت وغيرها من البيانات المتعاقبة. بفضل التعلم العميق، تعمل خوارزميات التسلسل والتعاقب بشكل أفضل بكثير مما كانت عليه قبل عامين فقط مما يتيح العديد من التطبيقات المثيرة في التعرف على الكلام والتركيبات الموسيقية وروبوتات الدردشة والترجمة الآلية وفهم اللغة الطبيعية وغيرها من التطبيقات الأخرى.

سوف تتعلم كيفية:

- فهم كيفية بناء وتدريب الشبكات العصبونية المتكررة (RNN) والمتغيرات الشائعة الاستخدام مثل GRUs والذاكرة طويلة المدى.

- تطبيق نماذج التسلسل على المشاكل الطبيعية للغة بما في ذلك تركيب النص.

- تطبيق نماذج التسلسل على التطبيقات الصوتية، بما في ذلك التعرف على الكلام والتركيبات الموسيقية.

هذا هو المساق الخامس والأخير من تخصص التعلم العميق.

تشاركdeeplearning.ai أيضًا مع مؤسسة نيفيديا للتعلم العميق في المساق الخامس، نماذج التسلسل، لتوفير مهمة برمجة على الترجمة الآلية مع التعلم العميق. سوف تتاح لك فرصة إنشاء مشروع تعلم عميق بمحتوى متطور وذو صلة بالعملية الصناعية.

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Syllabus

القوالب المتتابعة وآلية الانتباه

تعلم المزيد حول الشبكات العصبونية المتكررة. لقد ثبت أن هذا النوع من النماذج يعمل بشكل جيد للغاية على البيانات الزمنية. تحتوي على العديد من المتغيرات بما في ذلك LSTMs وGRUs والشبكات العصبونية المتكررة ثنائية الاتجاه، والتي سنتعرف عليها في هذا الجزء.

معالجة اللغة العصبية وكلمات التعارف

تعتبر معالجة اللغة الطبيعية مع التعلم العميق مزيجًا مهمًا. باستخدام تمثيلات متجهات الكلمات وطبقات التضمين، يمكنك تدريب الشبكات العصبونية المتكررة بأداء متميز في مجموعة متنوعة من الصناعات. ومن أمثلة التطبيقات تحليل المشاعر والتعرف على الكيانات المسماة والترجمة الآلية

يمكن زيادة القوالب المتتابعة باستخدام آلية الانتباه. ستساعد هذه الخوارزمية نموذجك على فهم المكان الذي يجب أن يركز فيه انتباهه في ضوء تسلسل المدخلات. ستتعرف هذا الأسبوع كذلك على التعرف على الكلام وكيفية التعامل مع البيانات الصوتية.

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Delves into advanced natural language processing, audio processing, and other sequential data

Taught by Andrew Ng, an acclaimed pioneer in deep learning and AI

Covers recurrent neural networks, a powerful class of models for sequential data

Provides hands-on experience through programming assignments on translation and other sequential data applications

Requires some prior understanding of deep learning and natural language processing, making it suitable for intermediate learners

Belongs to a specialization on deep learning, suggesting a comprehensive learning path

Career center

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

Conversational AI Engineer

The course "Sequential Models" can be useful for Conversational AI Engineers who want to build advanced conversational AI systems. This course covers RNNs, GRUs, LSTMs, and attention mechanisms, which are widely used in conversational AI systems. By mastering these techniques, you can develop models that can understand and generate natural language, making you a valuable asset in the field of conversational AI.

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Speech Recognition Engineer

The course "Sequential Models" can be useful for Speech Recognition Engineers who want to build advanced speech recognition systems. This course covers RNNs, GRUs, LSTMs, and attention mechanisms, which are widely used in speech recognition systems. By mastering these techniques, you can develop models that can accurately recognize speech, making you a valuable asset in the field of speech recognition and natural language processing.

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Natural Language Processing Engineer

The course "Sequential Models" can be useful for Natural Language Processing Engineers who want to build advanced NLP models. This course covers RNNs, GRUs, LSTMs, and attention mechanisms, which are the foundation of many state-of-the-art NLP models. By gaining expertise in these techniques, you can develop models for tasks such as machine translation, text summarization, and question answering, making you a valuable asset in the field of NLP.

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Natural Language Understanding Engineer

The course "Sequential Models" can be useful for Natural Language Understanding Engineers who want to build advanced NLP models. This course covers RNNs, GRUs, LSTMs, and attention mechanisms, which are the foundation of many state-of-the-art NLP models. By gaining expertise in these techniques, you can develop models for tasks such as machine translation, text summarization, and question answering, making you a valuable asset in the field of NLP.

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Machine Learning Researcher

The course "Sequential Models" can be useful for Machine Learning Researchers who want to specialize in developing sequential models. This course covers advanced topics such as RNNs, GRUs, LSTMs, and attention mechanisms. By mastering these techniques, you can develop models that can learn from sequential data, making it suitable for tasks such as natural language processing, speech recognition, and time series forecasting. This course can help you build a strong foundation for a successful career as a Machine Learning Researcher.

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Machine Learning Engineer

The course "Sequential Models" can be useful for Machine Learning Engineers who want to specialize in building models for sequential data. This course covers advanced topics such as RNNs, GRUs, LSTMs, and attention mechanisms. By mastering these techniques, you can develop models that can learn from sequential data, making it suitable for tasks such as natural language processing, speech recognition, and time series forecasting. This course can help you build a strong foundation for a successful career as a Machine Learning Engineer.

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Artificial Intelligence Engineer

The course "Sequential Models" can be useful for Artificial Intelligence Engineers who want to specialize in building models for sequential data. This course covers advanced topics such as RNNs, GRUs, LSTMs, and attention mechanisms. By mastering these techniques, you can develop models that can learn from sequential data, making it suitable for tasks such as natural language processing, speech recognition, and time series forecasting. This course can help you build a strong foundation for a successful career as an Artificial Intelligence Engineer.

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NLP Researcher

The course "Sequential Models" can be useful for NLP Researchers who want to specialize in developing sequential NLP models. This course covers advanced topics such as RNNs, GRUs, LSTMs, and attention mechanisms. By mastering these techniques, you can develop models that can learn from sequential data, making it suitable for tasks such as natural language processing, speech recognition, and time series forecasting. This course can help you build a strong foundation for a successful career as an NLP Researcher.

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Deep Learning Engineer

The course "Sequential Models" can be useful for Deep Learning Engineers who want to specialize in building models for sequential data. This course covers advanced topics such as RNNs, GRUs, LSTMs, and attention mechanisms. By mastering these techniques, you can develop models that can learn from sequential data, making it suitable for tasks such as natural language processing, speech recognition, and time series forecasting. This course can help you build a strong foundation for a successful career as a Deep Learning Engineer.

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Research Scientist

The course "Sequential Models" can be useful for Research Scientists who work on developing new machine learning algorithms and models. This course provides a deep understanding of RNNs, GRUs, LSTMs, and attention mechanisms, which are fundamental building blocks for sequential models. By understanding these techniques, you can contribute to the advancement of the field and develop innovative solutions for various applications, such as natural language processing, speech recognition, and time series forecasting.

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Data Analyst

The course "Sequential Models" can be useful for Data Analysts who work with sequential data. This course provides a solid foundation in RNNs, GRUs, and LSTMs, which are essential for modeling sequential data. Additionally, the course covers attention mechanisms, which are crucial for understanding the relationships between different parts of a sequence. By gaining expertise in these techniques, you can develop effective models for tasks such as natural language processing, speech recognition, and time series forecasting, enhancing your value as a Data Analyst.

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Data Scientist

The course "Sequential Models" can be useful for Data Scientists who work with sequential data. This course provides a solid foundation in RNNs, GRUs, and LSTMs, which are essential for modeling sequential data. Additionally, the course covers attention mechanisms, which are crucial for understanding the relationships between different parts of a sequence. By gaining expertise in these techniques, you can develop effective models for tasks such as natural language processing, speech recognition, and time series forecasting, enhancing your value as a Data Scientist.

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Software Engineer

The course "Sequential Models" can be useful for Software Engineers who want to develop applications that involve sequential data. This course provides a solid foundation in RNNs, GRUs, and LSTMs, which are essential for modeling sequential data. Additionally, the course covers attention mechanisms, which are crucial for understanding the relationships between different parts of a sequence. By gaining expertise in these techniques, you can develop robust and efficient software applications for tasks such as natural language processing, speech recognition, and time series forecasting.

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Computational Linguist

The course "Sequential Models" may be useful for Computational Linguists who want to develop advanced NLP models. This course covers RNNs, GRUs, LSTMs, and attention mechanisms, which are the foundation of many state-of-the-art NLP models. By gaining expertise in these techniques, you can develop models for tasks such as machine translation, text summarization, and question answering, making you a valuable asset in the field of NLP.

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Applied Scientist

The course "Sequential Models" can be useful for those pursuing a career as an Applied Scientist. This role involves developing and applying machine learning models to solve real-world problems. The course explores various types of sequential models, including recurrent neural networks (RNNs) and attention mechanisms, which are widely used in natural language processing (NLP) and speech recognition systems. By understanding these techniques, you can build models that can learn from sequential data, making it suitable for tasks such as language translation, text summarization, and speech transcription.

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