Model Optimization | Dropout, Regularization & Learning Rate Scheduler | Python AI/ML

🔥 Welcome to Python AI/ML Course Lecture 31! In this lecture, you will learn one of the most important Deep Learning topics: Model Optimization. We will understand how to improve CNN model performance and reduce overfitting using practical TensorFlow examples. 📌 Topics Covered: ✅ What is Model Optimization? ✅ Overfitting vs Underfitting ✅ What is Dropout? ✅ Why do we use Dropout? ✅ L1 vs L2 Regularization ✅ Learning Rate Explained ✅ Learning Rate Scheduler (ReduceLROnPlateau) ✅ CNN Practical Example in TensorFlow ✅ Real-Life Examples ✅ Interview Questions 💻 Practical Code Includes: ✔ CNN Model ✔ Conv2D ✔ MaxPooling2D ✔ Flatten Layer ✔ Dense Layer ✔ Dropout Layer ✔ L2 Regularization ✔ Learning Rate Scheduler ✔ Adam Optimizer ✔ Model Training & Evaluation 🎯 This lecture is perfect for: • Python Beginners • AI & Machine Learning Students • Deep Learning Learners • TensorFlow Beginners • University Students • Interview Preparation 👍 If you found this lecture helpful, don't forget to: ✔ Like ✔ Share ✔ Subscribe ✔ Turn on the notification bell 🔔 #Python #AI #MachineLearning #DeepLearning #TensorFlow #CNN #Dropout #Regularization #LearningRate #LearningRateScheduler #PythonTutorial #ArtificialIntelligence #DataScience #NeuralNetworks #ModelOptimization