Computer Vision

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About the Course

WeCloud’s Computer Vision course offers an in-depth study of the key concepts, algorithms, and applications in computer vision, focusing on image data, CNNs, transfer learning, and generative models. Students will gain practical skills to develop and optimize vision-based solutions for real-world challenges.

Curriculum

  • Module 1: Computer Vision Basics

    Overview:

    This module explains the basic concept of Computer Vision.

    Tools to Cover:

    • Image Data Processing
    • Video Data Processing
    • 3D LiDAR Data Processing
  • Module 2: Data Preparation

    Overview:

    This module shows the fundamentals of image data preparation, including image acquisition, normalization, and processing techniques.

    Tools to Cover:

    • Image Annotation
    • Image Data Augmentation
    • Generate Synthetic Data
  • Module 3: Convolutional Neural Networks

    Overview:

    This module introduces to CNN architectures and their significance in computer vision tasks.

    Tools to Cover:

    • CNN Introduction
    • Image Classification
    • Vision Model Libraries
    • VGG
    • ResNet

  • Module 4: Fine-tuning & Transfer Learning

    Overview:

    This module explores model tuning techniques and the concept of transfer learning to adapt pre-trained models for specific tasks.

    Tools to Cover:

    • Computer Vision Model Tuning
    • Transfer Learning

  • Module 5: CNN Use Cases

    Overview:

    This module examines various applications of CNNs, including object detection, semantic segmentation, and instance segmentation.

    Tools to Cover:

    • Object Detection
    • Semantic Segmentation
    • Seq2Seq
    • Yolov5

  • Module 6: Generative Vision Models

    Overview:

    This module delves into advanced topics such as Generative Adversarial Networks (GANs), diffusion models, and synthetic data generation.

    Tools to Cover:

    • GAIN
    • VAE
    • Diffusion Models
    • Synthetic Image Data using GANs

Learning Outcomes

By the end of this course, participants will be able to:

  • Describe the principles of computer vision and the importance of image data processing.
  • Prepare and annotate image datasets for machine learning applications.
  • Implement convolutional neural networks and understand their architecture and functionality.
  • Apply transfer learning techniques to improve model performance on specific tasks.
  • Demonstrate proficiency in various CNN use cases, including object detection and segmentation.
  • Understand and implement generative models for synthetic data generation and creative applications in computer vision.

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FAQ

Frequently asked questions about the bootcamp

The course is structured into weekly modules, each containing video lectures, reading materials, assignments, and quizzes. You can complete the modules at your own pace, but we recommend following the weekly schedule to stay on track.

You can get support in multiple ways:

  • TA Support on Slack: Our teaching assistants are available on Slack to answer your questions and provide guidance.
  • Peer Community on Discord: Join our Discord community to discuss course topics, share ideas, and collaborate with fellow students.

TAs are available on Slack from 9 AM to 6 PM (ET) Monday to Friday. Outside these hours, you can still post your questions, and TAs will respond as soon as they are back online.

After enrolling in the course, you will receive an invitation link to join the Discord community. Follow the link to create an account or log in to your existing account.

The Discord community offers peer-to-peer support, where you can discuss course topics, share resources, collaborate on projects, and network with fellow learners

The optional mentoring service includes one-on-one sessions with an experienced mentor who can provide personalized guidance, feedback on your progress, and help you set and achieve your learning goals.

Please talk to our Program Advisors to sign up for Mentorship services for an additional cost

Yes, you will have lifetime access to the course materials, including any updates made to the content in the future.

We accept all major credit cards, PayPal, and bank transfers. You can choose your preferred payment method at checkout

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