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.
النماذج المتعاقبة
Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
15 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
3 assignments
Taught in Arabic
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There are 3 modules in this course
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.
What's included
12 videos2 readings1 assignment3 programming assignments3 ungraded labs
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
What's included
10 videos1 reading1 assignment2 programming assignments2 ungraded labs
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.
What's included
11 videos3 readings1 assignment2 programming assignments2 ungraded labs
Offered by
Recommended if you're interested in Machine Learning
Alfaisal University | KLD
Duke University
Macquarie University
Coursera Project Network
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