Neural Networks and Deep Learning

About this course

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 surfaces
• Practical walkthroughs for regularization methods using real-world scenarios
• Hands-on use cases demonstrating the power of neural networks in real-world problems
• Emphasis on interpreting and improving model performance

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