Real World 5+ Deep Learning Projects Complete Course

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Published 1/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 14m | Size: 1.5 GB

Learn Real World 5+ Deep Learning Projects Complete Course Using Roboflow and Google Colab

[b]What you'll learn[/b]
Understand how to integrate Roboflow into the project workflow, leveraging its features for efficient dataset management, augmentation, and optimization for bot
Explore the process of collecting and preprocessing datasets for both facial recognition and emotion detection, ensuring the data is optimized for training a YO
Dive into the annotation process, marking facial features on images for recognition and labeling emotions for detection. Train YOLOv7 models for accurate and ro
Explore the end-to-end training workflow of YOLOv7 using the annotated and preprocessed datasets, adjusting parameters, and monitoring model

[b]Requirements[/b]
Access to a computer with internet connectivity.
Basic understanding of machine learning and computer vision concepts.

[b]Description[/b]
Course Title: Real World 5+ Deep Learning Projects Complete Course Using Roboflow and Google Colab
Course Description
Welcome to the immersive "Learn Facial Recognition And Emotion Detection Using YOLOv7: Course Using Roboflow and Google Colab." In this comprehensive course, you will embark on a journey to master two cutting-edge applications of computer vision: facial recognition and emotion detection. Utilizing the powerful YOLOv7 algorithm and leveraging the capabilities of Roboflow for efficient dataset management, along with Google Colab for cloud-based model training, you will gain hands-on experience in implementing these technologies in real-world scenarios.
What You Will Learn
Introduction to Facial Recognition and Emotion Detection
Understand the significance of facial recognition and emotion detection in computer vision applications and their real-world use cases.
Setting Up the Project Environment
Learn how to set up the project environment, including the installation of necessary tools and libraries for implementing YOLOv7 for facial recognition and emotion detection.
Data Collection and Preprocessing
Explore the process of collecting and preprocessing datasets for both facial recognition and emotion detection, ensuring the data is optimized for training a YOLOv7 model.
Annotation of Facial Images and Emotion Labels
Dive into the annotation process, marking facial features on images for recognition and labeling emotions for detection. Train YOLOv7 models for accurate and robust performance.
Integration with Roboflow
Understand how to integrate Roboflow into the project workflow, leveraging its features for efficient dataset management, augmentation, and optimization for both facial recognition and emotion detection.
Training YOLOv7 Models
Explore the end-to-end training workflow of YOLOv7 using the annotated and preprocessed datasets, adjusting parameters, and monitoring model performance for both applications.
Model Evaluation and Fine-Tuning
Learn techniques for evaluating the trained models, fine-tuning parameters for optimal performance, and ensuring robust facial recognition and emotion detection.
Deployment of the Models
Understand how to deploy the trained YOLOv7 models for real-world applications, making them ready for integration into diverse scenarios such as security systems or human-computer interaction.
Ethical Considerations in Computer Vision
Engage in discussions about ethical considerations in computer vision, focusing on privacy, consent, and responsible use of biometric data in facial recognition and emotion detection.

Who this course is for
Students and professionals in computer vision, artificial intelligence, or human-computer interaction.
Developers interested in mastering YOLOv7 for multiple computer vision applications.

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