22. Why AI Sometimes Learns Too Fast and Breaks

In this lesson, you will learn why AI models sometimes learn too fast and fail during training by exploring the concepts of learning rate, training instability, and exploding gradients. We explain how gradient descent updates model parameters, why choosing the wrong learning rate can lead to oscillation or divergence, how exploding gradients develop in deep neural networks, and the practical techniques used to stabilize training, including gradient clipping, better weight initialization, adaptive optimizers, batch normalization, and learning rate scheduling. The lesson includes intuitive explanations, worked examples, Python programming demonstrations, and practice problems, making it ideal for students, researchers, data scientists, and anyone studying machine learning, deep learning, artificial intelligence, neural networks, or PyTorch. #EJDansu #Mathematics #Maths #MathswithEJD #Goodbye2024 #Welcome2025 #ViralVideos #Trending #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks #GradientDescent #LearningRate #ExplodingGradients #Backpropagation #AIEngineering #DataScience #Python #PyTorch #TensorFlow #AIEducation #ComputerScience #MLTutorial #AITutorial #NeuralNetworkTraining #GenerativeAI #LearnAI -Feel very free to follow me on all channels: https://linktr.ee/ejdansu -Tech-relevant playlists: Basic Calculus    • Basic Calculus   Data Analysis with Python    • Data Analysis with Python   Game Theory    • Game Theory   Graph & Network Theory    • Graph & Network Theory   Linear Programming    • Linear Programming   Matrices    • Matrices   Numerical Analysis    • Numerical Analysis   Numerical Optimisation Techniques    • Numerical Optimisation Techniques   Probability    • Probability   Scientific Computing with Python    • Scientific Computing with Python   Set Theory    • Set Theory   SQL with Python    • SQL with Python   Statistics    • Statistics   Vectors    • Vectors