DL1.1- From Linear Regression to Neural Networks скачать в хорошем качестве

DL1.1- From Linear Regression to Neural Networks

🧠 Complete Introduction to Deep Learning: Mathematical Foundations & Intuition Join Dr. Heman Shakeri for a comprehensive introduction to deep learning that builds from the ground up! This lecture bridges the gap between traditional machine learning and modern neural networks, providing both mathematical rigor and geometric intuition. 📚 What You'll Learn: ✅ The fundamentals of supervised learning and why generalization is challenging ✅ Linear regression as the foundation of neural networks ✅ Bias-variance trade-off and regularization techniques (Ridge vs Lasso) ✅ From linear boundaries to exponential expressivity with ReLU neurons ✅ The geometric revolution: how neurons partition input space ✅ Loss functions and the optimization landscape ✅ Stochastic Gradient Descent (SGD) - the engine powering modern AI ✅ Why deep networks have exponential expressive power ✅ Universal approximation theorem and hierarchical feature learning 🎯 Perfect for: Students beginning their deep learning journey Researchers wanting solid mathematical foundations ML practitioners seeking geometric intuition Anyone curious about how neural networks really work 🔬 Key Concepts Covered: #DeepLearning #MachineLearning #NeuralNetworks #Mathematics #SGD #Optimization #LinearRegression #BiasVariance #ReLU #GradientDescent 💡 This lecture provides the mathematical foundation you need to understand modern deep learning. No black boxes - just clear explanations of the principles that power AI! 🔔 Subscribe for more deep learning content and hit the bell for notifications! 📖 Course Resources: 🌐 Course Website: https://deep-learning-course.uva.edu 📁 GitHub Repository: https://github.com/shakeri-heman/deep... 🎓 About the Instructor: Dr. Heman Shakeri, PhD - UVA School of Data Science All course materials are freely available. Star the GitHub repo to stay updated with new content!