Course / Course Details

Deep Learning (DL)

  • Dr. Anas Bilal image

    By - Dr. Anas Bilal

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Course Requirements

  • Basic knowledge of machine learning, programming, and statistics.
  • Active participation in lectures, labs, and project activities.
  • Completion of assignments, quizzes, and practical exercises.
  • Use of deep learning frameworks for model development and experimentation.

  • Successful completion of a deep learning project addressing a real-world problem.

    • Course Description

    This course provides an in-depth study of Deep Learning techniques and architectures used to solve complex problems in computer vision, natural language processing, speech recognition, and other AI applications. Students will explore neural network architectures, optimization methods, and advanced deep learning models through practical implementation and real-world case studies.

    Course Outcomes

    Upon successful completion of this course, students will be able to:

    1. Explain the fundamental concepts, architectures, and learning mechanisms of deep neural networks.
    2. Apply deep learning techniques to solve real-world problems involving image, text, and sequential data.
    3. Evaluate the performance of deep learning models using appropriate optimization and validation methods.
    4. Develop and deploy deep learning solutions using modern frameworks and best practices.

    Course Curriculum

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    Instructor

    Dr. Anas Bilal

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    • Category: Data Science and Analytics

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    • Language: English

    • Skill Level Beginner

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