Intermediate

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.

4.6
|2h 31m
ANN: Learning Like the Human Brain course

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

1

Module 1: Foundations of Biological and Artificial Neural Networks

15m
2

Module 2: Multi-Layer Perceptrons and Forward Propagation

17m
3

Module 3: Backpropagation, Loss Functions, and Gradient Descent

21m
4

Practical Project - 1

16m
5

Module 4: Optimizers, Batching, and Overfitting Prevention

22m
6

Module 5: Deep Learning Frameworks and Implementation

22m
7

Module 6: Specialized Networks and Real-World Deployment

21m
8

Practical Project - 2

17m

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.

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