Deep Learning in Practice III: Face Recognition

🚀 Course Title: Deep Learning in Practice III: Face Recognition 🆕

About the Course 🎓

Welcome to the Journey of Mastering Face Recognition!

Your Instructor: Anis Koubaa Course Focus: Python, OpenCV, MTCNN, FaceNet with TensorFlow and Keras for Real-World Face Recognition Systems 🖼️🤯

What's Inside the Course?

This course is a goldmine for anyone looking to dive into the world of face recognition using deep learning techniques. It's the third installment in the Deep Learning in Practice series, and it builds upon the foundations laid down by my previous courses on object classification and transfer learning with TensorFlow and Keras.

Here's what you'll cover:

• Face Extraction: Learn to extract faces from images using MTCNN (Multi-task Cascaded Convolutional Networks).
• Face Localization: Discover how to localize faces within images by identifying their bounding boxes.
• Face Embedding: Understand the process of creating a unique 'fingerprint' for each face, known as a face embedding with FaceNet.
• Database Storage and Comparison: Store face embeddings in a database and learn how to compare them for identification purposes.

Why is this course crucial?

• Real-World Applications: Face recognition systems are integral to modern applications, including surveillance, access control, and biometric identification.
• Comprehensive Learning Path: This course offers a thorough understanding of the topic, saving you from the headache of scouring the internet for information.

Why You'll Benefit From This Course:

• Essential Theories and Practical Skills: Get a solid grasp of both the theoretical underpinnings and practical applications of face recognition systems.
• Step-by-Step Guidance: Follow along with Python programming, OpenCV, MTCNN, FaceNet, TensorFlow, and Keras to build your own face recognition application.
• Hands-On Learning: Utilize a provided Jupyter notebook to write code and apply concepts in real-time during the course.
• Expert Insights: I'll guide you through every step, ensuring you understand the complete process of face recognition by the end of this course.

Pre-requisites:

• Python Knowledge: A basic understanding of Python programming is essential to get the most out of this course.
• Deep Learning Basics (Optional): While not mandatory, a foundational knowledge of deep learning and TensorFlow would be advantageous but is not required as I'll cover these basics within the course.
• Prior Courses Recommended: If you haven't yet, consider taking my first two courses in the Deep Learning in Practice series for a smoother experience with this course on face recognition.

About Your Instructor 👨‍🏫

I am Anis Koubaa, a Full Professor in Computer Science and the Leader of the Robotics and Internet-of-Things Lab at Prince Sultan University. My passion for sharing knowledge has led me to author not one, but two best-seller courses on Deep Learning and ROS (Robot Operating System).

This course, Deep Learning in Practice III: Face Recognition, is a testament to my commitment to making advanced topics like deep learning accessible and engaging for beginners. If you're ready to embark on an exciting learning journey, I'm here to guide you through the wonders of face recognition technology.

Ready to unlock the secrets of face recognition? 🛠️👀 Join me, Anis Koubaa, in this enlightening course, and let's bring your face recognition project to life! Let's get coding and exploring the fascinating world of AI together. 🚀💻