ANN: Learning Like the Human Brain
Artificial Neural Networks (ANN) are machine learning models inspired by the human brain. They consist of interconnected layers of neurons that learn patterns from data, making them powerful for tasks like image recognition, prediction, and complex decision-making.

What's Included
8
Lessons
2h 31m
Duration
Certificate
What You'll Master
Skills and outcomes you'll walk away with
Module 1: Foundations of Biological and Artificial Neural Networks
Module 2: Multi-Layer Perceptrons and Forward Propagation
Module 3: Backpropagation, Loss Functions, and Gradient Descent
Practical Project - 1
Module 4: Optimizers, Batching, and Overfitting Prevention
Module 5: Deep Learning Frameworks and Implementation
Course Curriculum
8 lessons • 2h 31m total
Module 1: Foundations of Biological and Artificial Neural Networks
Module 2: Multi-Layer Perceptrons and Forward Propagation
Module 3: Backpropagation, Loss Functions, and Gradient Descent
Practical Project - 1
Module 4: Optimizers, Batching, and Overfitting Prevention
Module 5: Deep Learning Frameworks and Implementation
Module 6: Specialized Networks and Real-World Deployment
Practical Project - 2
Certification Path
Certification Exam
18 multiple-choice questions • 70% passing score required
Final Project: Designing a Biologically-Inspired Neural Network Architecture
In this final assignment, you are tasked with designing and implementing a neural network that incorporates at least two core principles of biological learning discussed in the course, such as Hebbian plasticity, spiking dynamics, or neuromodulation. You will apply this architecture to a reinforcement learning or pattern recognition task. The project requires a functional codebase, a report detailing how the selected biological mechanisms were translated into computational components, and an analysis of how these mechanisms affect the model's convergence or robustness compared to standard backpropagation-based models.
Verified Certificate
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