Neural Networks and Deep Learning

Are you ready to dive deep into the powerful world of Neural Networks and Deep Learning. Whether you're a student, data science enthusiast, or an early-career AI professional, this course will help you build a solid foundation in modern neural architectures - from perceptrons to multi-layered networks - and master the mechanics behind how they learn. What You'll Learn:Understand what neural networks are and how they're inspired by the human brain.

Build simple ANNs from scratch for basic logic operations (OR, AND, NAND). Dive into Perceptrons and Multi-Layer Perceptrons (MLP), learning how they process data through forward propagation. Master key concepts like loss functions, cost functions, and gradient descent, including the difference between partial derivatives and gradients.

Implement and compare optimization techniques like batch, stochastic, mini-batch, momentum, and RMSProp gradient descent. Learn how to prevent overfitting using techniques such as L1/L2 regularization, dropout, and batch normalization. Apply these concepts in practical use cases, including water quality contamination detection and MNIST digit recognition.

Key Highlights:Visual and intuitive explanations for gradient descent and error surfacesPractical walkthroughs for regularization methods using real-world scenariosHands-on use cases demonstrating the power of neural networks in real-world problemsEmphasis on interpreting and improving model performance.