Learn Machine Project Face Recognition Using TensorFlow And Keras From Scratch
What you'll learn
Gain a comprehensive understanding of the principles and applications of face recognition
Familiarize yourself with the basics of TensorFlow and Keras, understanding their role in building neural networks for face recognition.
Explore techniques for collecting and preprocessing face data, ensuring high-quality input for training your models.
Understand the process of training your face recognition model using TensorFlow and Keras, optimizing for accuracy and efficiency.
Requirements
Basic understanding of machine learning concepts.
Familiarity with Python programming.
Download Pycharm IDE For Project
Teachable machine Basic Understanding
Description
Course Title: Face Recognition Using TensorFlow and Keras From ScratchCourse Description:Welcome to the Face Recognition Using TensorFlow and Keras From Scratch course, where you'll delve into the fascinating world of machine learning and computer vision to build a robust face recognition system. This comprehensive course is designed for both aspiring machine learning enthusiasts and developers aiming to expand their skill set. Through hands-on exercises and real-world applications, you will gain the knowledge and expertise needed to implement face recognition from scratch using TensorFlow and Keras.What You Will Learn:Introduction to Face Recognition:Explore the principles, applications, and significance of face recognition in various domains.Setting Up Your Development Environment:Configure and set up your development environment for TensorFlow and Keras, ensuring a smooth coding experience.Foundations of TensorFlow and Keras:Gain a solid understanding of the basics of TensorFlow and Keras, the essential tools for building neural networks in face recognition.Data Collection and Preprocessing:Learn techniques for collecting and preprocessing face data to ensure high-quality input for training your face recognition models.Building a Convolutional Neural Network (CNN):Dive into the architecture of Convolutional Neural Networks (CNNs), tailoring them for face recognition tasks.Training and Fine-Tuning the Model:Understand the process of training your face recognition model, optimizing it for accuracy, and fine-tuning its parameters for optimal performance.Integration with OpenCV for Real-Time Applications:Integrate your trained face recognition model with OpenCV, a powerful computer vision library, for real-time applications.Handling Real-World Challenges:Address challenges such as pose variation, lighting conditions, and occlusions to enhance the robustness of your face recognition system.Security and Ethical Considerations:Explore the security implications and ethical considerations in face recognition applications, emphasizing responsible deployment practices.Who Is This Course For:Machine Learning EnthusiastsDevelopers and ProgrammersComputer Vision EnthusiastsStudents and ResearchersRequirements:Basic understanding of machine learning concepts.Familiarity with Python programming.Access to a computer with TensorFlow and Keras installed.Why Enroll:Hands-On Project: Engage in a comprehensive hands-on project to reinforce your learning.Real-World Applications: Acquire skills applicable to real-world face recognition scenarios.Community Support: Join a community of learners, share experiences, and seek assistance from instructors and peers.Embark on this exciting journey to master face recognition using TensorFlow and Keras. Enroll now and take the first step toward becoming proficient in implementing cutting-edge machine learning applications!
Overview
Section 1: INTRODUCTION TO COURSE
Lecture 1 Introduction To Teachable Machine
Lecture 2 CREATING DATASET FOR FACE RECOGNITION
Lecture 3 TRAIN THE DATASET AND ITS FEATURES
Lecture 4 VALIDATE THE TRAINED MODEL
Lecture 5 DOWNLOAD THE TENSORFLOW AND KERAS MODEL
Lecture 6 EXTRACT THE KERAS MODEL AND LABELS
Section 2: INTRODUCTION TO Face Recognition Using TesnsorFlow And Keras
Lecture 7 INSTALL PACKAGES AND EXECUTE PROJECT IN PYCHARM IDE
Lecture 8 OUTPUT FOR FACE RECOGNITION AND CONCLUSION
Machine Learning Enthusiasts,Developers and Programmers,Computer Vision Enthusiasts