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    <h2>Information </h2>
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      <h5>Instructors: R. Venkatesh Babu </h5>
      <h5>Teaching Assistant: Aakash Kumar Singh, Badrinath Singhal and Priyam Dey </h5>
      <h5>Classroom Venue: Room No. 102, CDS Department</h5>
      <h5>First Class: Wednesday, 8 Jan 2025, at 05:00 PM at CDS-202 classroom</h5>
      <h5> Regular Class Timings (from 14th Jan 2025): Tue 15:30 - 17:00, Thu 15:30 - 17:00, Location: CDS 102 seminar room </h5>
      <h5> Course Registration Form: <a href= 'https://forms.gle/riz8KVCAMzVhe4As8' target='blank'> Link to Google Form</a> It is mandatory to fill this form for interested students</h5>
      <h5>Teams Code (2025 DLCV): 47wb8ek</h5>
<!--       <h5 style="color: red;">Please note: Tomorrow's class will be replaced by a talk by Dr. Ekdeep Singh Lubana at 4:00 PM in CDS 102. Attendance is mandatory for DS 265 students.</h5> -->
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    <h2>Updates</h2>
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            <li style="color: red;">13th Jan 2025: There will be class tomorrow (14th Jan 2025) at 3:30 PM in the 102 Seminar Room, despite it being an institute holiday.
                                                    The purpose of this session is to discuss the new class timings. It is mandatory for all students to be present, as no further discussions regarding the class timings will be entertained after this meeting.
                                                    Please be on time and make sure to attend.</li>
            <li style="color: red;">8th Jan 2025: Class on 9th Jan 2025 will be replaced by a talk by Dr. Ekdeep Singh Lubana at 4:00 PM in CDS 102. Attendance is mandatory for DS 265 students.</li>
            <li style="color: red;">8th Jan 2025: Permission for Google form is updated.</li>
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<!--         <h5 style="color: red;">8th Jan 2025: Class on 9th Jan 2025 will be replaced by a talk by Dr. Ekdeep Singh Lubana at 4:00 PM in CDS 102. Attendance is mandatory for DS 265 students.</h5> -->
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      <h2>Brief description of the course</h2>

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<p>In the recent years, Deep Learning has pushed to boundaries of research in many fields. This course focuses on the application of Deep Learning in the field of Computer Vision. The first half of the course formulates the basics of Deep Learning, which are built on top of various concepts from Image Processing and Machine Learning.  The second half highlights the various flavors of Deep Learning in Computer Vision, such as Generative Models, Recurrent Models, and Deep Reinforcement Learning Models, 3D vision as well as foundational models such as Stable Diffusion.
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      <a href="{{ site.baseurl }}/about" class="read-more">Read More</a>

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    <h2>Prerequisites</h2>
    <p>Primary crucial prerequisites : Machine Learning and Computer Vision/Image Processing
    </p><p>Secondary Prerequisites(familiarity preffered): Probability, Statistics and Linear Algebra.
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    <h2>Course Outcomes</h2>
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<li>Thoroughly Understanding the fundamentals of Deep Learning.</li>
<li>Gaining knowledge of the different modalities of Deep learning currently used.</li>
<li>Gaining Knowlegde about State-of the art models and Other Important Works in recent years.</li>
<li>Learning the skills to implement Deep Learning based AI Systems(Use of Multiple packages etc.)</li>
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