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Deep Learning with Computer Vision

Course Description

Introduction:
In the rapidly evolving landscape of technology, the fusion of Machine Learning (ML) and Deep Learning (DL) has propelled Computer Vision to the forefront of innovation. This interdisciplinary field within Artificial Intelligence (AI) focuses on enabling machines to interpret and comprehend visual data, mirroring human visual perception. From autonomous vehicles navigating complex environments to facial recognition systems identifying individuals in real-time, the applications of computer vision are vast and transformative. In this comprehensive guide, we will explore the intricacies of computer vision, delving into foundational concepts, advanced techniques, and practical applications.

Foundations of Computer Vision:
At the heart of computer vision lies the understanding of how machines perceive and analyze images and videos. Our journey begins with an exploration of fundamental concepts such as image representation, feature extraction, and image classification. We'll unravel the workings of Convolutional Neural Networks (CNNs), a cornerstone of modern computer vision, and understand their role in tasks like object detection and segmentation. Additionally, we'll delve into OpenCV, a powerful library for image processing and manipulation, and learn how to leverage its capabilities to build robust computer vision applications.

Machine Learning and Deep Learning in Computer Vision:
Machine Learning and Deep Learning algorithms form the backbone of modern computer vision systems. We'll delve into the principles of ML and DL, exploring their applications in tasks such as image recognition, object tracking, and scene understanding. Through hands-on projects and practical examples, you'll gain proficiency in training neural networks, optimizing model performance, and deploying solutions in real-world scenarios. Moreover, we'll cover advanced topics like Generative Adversarial Networks (GANs), Transfer Learning, and Large Language Models (LLMs), pushing the boundaries of what's possible in computer vision.

Specialized Courses:
Whether you're a novice looking to kickstart your journey into computer vision or an experienced practitioner aiming to expand your skill set, our specialized courses cater to learners of all levels. Beginners can enroll in introductory courses that provide a solid foundation in image processing, ML, and DL. For those seeking more advanced knowledge, specialized tracks in advanced computer vision with TensorFlow and CNNs offer a deeper dive into cutting-edge techniques and methodologies. Our curriculum is designed to be flexible, allowing learners to tailor their learning experience based on their interests and expertise.

Practical Applications:
The true essence of learning lies in its application. Throughout our courses, you'll have the opportunity to work on real-world projects that demonstrate the practical implications of computer vision. From building a facial recognition system to detecting anomalies in various images, you'll apply your knowledge to solve complex problems and gain invaluable hands-on experience. Additionally, guest lectures and industry insights from experts in the field will provide you with a holistic understanding of how computer vision is shaping various industries, from healthcare and automotive to entertainment and retail.

Future Directions:
As technology continues to advance, the future of computer vision holds boundless possibilities. From enhancing accessibility for individuals with disabilities to revolutionizing the way we interact with augmented reality, the impact of computer vision will continue to reverberate across society. Our courses are designed to equip you with the skills and knowledge needed to navigate this ever-changing landscape, empowering you to drive innovation and make meaningful contributions to the field of computer vision.

Conclusion:
In conclusion, mastering computer vision requires a blend of theoretical understanding, practical skills, and a passion for innovation. Whether you're embarking on your learning journey or seeking to expand your expertise, our comprehensive courses offer a roadmap to success in this dynamic field. From foundational concepts to advanced techniques, we provide the resources and support needed to unlock the full potential of computer vision. Enroll today and embark on a transformative learning experience that will shape the future of technology.

 

Module - 01

  • Intorduction to Image Processing

Module - 02

  • Image Enhancement

Module - 03

  • Image Restoration

Module - 04

  • Image Segmentation

Module - 05

  • Image Representation and Description

Module - 06

  • Tensorflow Framework

Module - 07

  • Pytorch Framework

Module - 08

  • Theory: Data
  • Label
  • Model

Module - 09

  • Theory: Model Tuning
  • Model Regularization

Module - 10

  • Theory: Elementary Statistics

Module - 11

  • Theory: Elementary Probability

Module - 12

  • Implementation: Data Setup and Loader

Module - 13

  • Implementation: Label Setup with Insights Elements

Module - 14

  • Implementation: Model Design

Module - 15

  • Implementation: LoM Function

Module - 16

  • Task 1: Let's Build a Image Classifier

Module - 17

  • Task 2: Let's Build a Image Segmentation Process

Module - 18

  • Task 3: Let's Build a Semantic Segmentation

Module - 19

  • Task 4: Let's Build a Object Detection

Module - 20

  • Task 5: Let's Build a Action Recognition System

Module - 21

  • Task 6: Let's Build a Image Denoising

Module - 22

  • Task 7: Let's Build a super resolution

Module - 23

  • Task 8: Let's Build GAN

Module - 24

  • Task 9: Let's Build Diffusion Model

Module - 25

  • Task 10: Let's Learn Self Supervised Part 1

Module - 26

  • Task 11: Let's Learn Self Supervised Part 2

Module - 27

  • Task 12: Let's Learn Contrastive Learning

Module - 28

  • Task 13: Let's Code an Image Net Model

Module - 29

  • Self Project Phase

Nazmus Saqib

Education

  • Chosun University, Gwangju,South Korea. M.Sc in Computer Engineering, 2023-2021
  • Khulna University of Engineering & Technology (KUET) B.Sc in Electronics & Communication Engineering (ECE), 2012-2018

IT Industrial Experience (6 Years)

No. Institution Designation Duration
1 Computer Vision Lab, Chosun University Research Engineer February 2023 – Present
2 Computer Vision Lab, Chosun University Graduate Research Assistant March 2021 – February 2023
3 Creative Engineers Ltd. Engineer July 2018 – July 2020

Project/Research

  • February 2023: Multi-inference strategy for self-supervised denoising
  • September 2022: Rethinking Gradient Weight’s Influence Over Saliency Map Estimation
  • September 2022: Denoising Single Images by Feature Ensemble Revisited
  • March 2023: Semi-supervised atmospheric component learning for low-light imaging problem
  • March 2020: An Improved Adaptive Optimization Technique For Image ClassificationFebruary, 2020: Image Classification Using DNN With An Improved Optimizer
  • March 2021 - Present: FRVT—Face recognition Project | PL: Python, C++, OS: Linux/Ubuntu
  • December 2021: Fingerprint liveness detection | Python

Evaluation

Image

Will You Get a Certificate After the Course:
Yes, after completing the course you will achieve the certificate. There are 3 Types of Certification available based on assessment in our each course. These types are mentioned below:

  1. General Certificate (If the total marks are between 60% and 70%)
  2. White Belt Certificate (If the total marks are between 70% and  90%)
  3. Black Belt Certificate (If the total marks are between 90% and 100%)

How do we evaluate a Student for certificate:
Our Evaluation Process is extremely simple. Our Evaluation process is maintained by following steps:

  1. Class Attendance (between 60 - 100%)
  2. Assignment Submission (Bonus Point for Quick Submission, As Usual Points For On-Time Submission, Minus point for late submission)
  3. Mock Test (Written + Viva)
  4. Project Submission (Bonus Point for Quick Submission, As Usual, Points For On-Time Submission, Minus points for late submission)
  5. Soft Skills

Please note that the evaluation always be done by the internal and external judges via a blind review process

 

How do we recommend a Student for the job:
After completing the course, the white and black belt-certificated students will be allowed to join our stem-learning-based boot camp. The students will need to complete the boot camp with proper instruction and discipline. After completing the boot camp, we will recommend the desired candidate to any company in sha Allah.

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Reviews through Social Media

Tasfiq Kamran

Aminul Mahi

Shaiful Islam

MD Asadullah Shibli

Obaydullah Hasib

Nirban Mitra Joy

Md Maniruzzaman Manir

Md Anower Hossain

Mehedi Azad

Alomghir Hossain

Video Feedback

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Key Topics:

  • Image Processing
  • Tensforflow & Pytorch Framewok
  • Data Setup and Loader
  • Generative Al & Diffusion Model
  • Model Design
  • Classification & Segmentation
  • Self Supervised and Contrastive Learning Model

Price

5000

Discount Price

4000

Duration

40-45 Classes

Available Seats

45

Class Type

Live

Access

Lifetime

Time

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