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.

سوف تتعلم كيفية:

- 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.

What's inside

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

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 النماذج المتعاقبة with these activities:

Organize Course Materials

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Keep course materials organized for easy access and effective revision

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Create a dedicated folder for course materials
Categorize and store notes, assignments, and quizzes

Review of Calculus and Linear Algebra

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Refresh your knowledge of essential mathematical concepts for a stronger foundation in deep learning

Browse courses on Calculus

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Revisit key concepts in Calculus and Linear Algebra
Solve practice problems and exercises

AI and Deep Learning Meetup

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Connect with professionals in the field of AI and deep learning to expand your knowledge and network

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Identify and attend AI and deep learning meetups
Engage in discussions and exchange ideas

Five other activities

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RNN Problem-Solving Sessions

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Engage with peers to discuss RNN-related problems, fostering collaboration and deeper understanding

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Form study groups or join online forums
Discuss and solve RNN-based problems together
Share insights and learn from different perspectives

RNN Exercises using Tensorflow

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Use Tensorflow's exercises on Recurrent Neural Networks to reinforce your understanding of sequence data modeling

Browse courses on Recurrent Neural Networks

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Follow the exercises provided in Tensorflow's official tutorial
Experiment with different parameters and architectures

Build a Chatbot using RNN

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Combine RNNs with NLP techniques to create a chatbot, showcasing the practical applications of sequence models

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Choose a suitable NLP library like NLTK or spaCy
Design the chatbot's architecture and training strategy
Train and evaluate the chatbot

RNN Workshop: Theory and Applications

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Attend a workshop focused on RNNs to gain practical insights and hands-on experience

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Research and identify relevant workshops
Register and participate in the workshop

Natural Language Generation Project

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Apply RNNs to generate natural language text, demonstrating your proficiency in using sequence models for language-related tasks

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Define the scope and goals of your project
Choose a suitable dataset for training
Build and train an RNN model
Evaluate and improve the model's performance

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.

See salaries and explore the career path for Conversational AI Engineer

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.

See salaries and explore the career path for Speech Recognition Engineer

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.

See salaries and explore the career path for Natural Language Processing Engineer

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.

See salaries and explore the career path for Natural Language Understanding Engineer

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.

See salaries and explore the career path for Machine Learning Researcher

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Artificial Intelligence Engineer

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.

See salaries and explore the career path for NLP Researcher

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.

See salaries and explore the career path for Deep Learning Engineer

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Computational Linguist

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.

See salaries and explore the career path for Applied Scientist

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