Neural Networks: Building the Brains of Artificial Intelligence

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Course Modules:

Module 1: Introduction to Neural Networks

What is a neural network?

Biological inspiration vs. artificial implementation

Real-world applications across industries

Module 2: Structure and Components

Neurons, weights, biases, and layers

Activation functions (ReLU, Sigmoid, Tanh)

Feedforward vs. feedback (recurrent) networks

Module 3: Forward Propagation & Backpropagation

How data flows through the network

Cost functions and gradient descent

Training with backpropagation step-by-step

Module 4: Training Neural Networks

Learning rate, epochs, and batch size

Avoiding overfitting with regularization and dropout

Model tuning and performance metrics

Module 5: Building Neural Networks in Python

Using TensorFlow and PyTorch

Creating models from scratch

Training and evaluating on real datasets (e.g., MNIST, Iris)

Module 6: Advanced Network Types (Preview)

Introduction to Convolutional Neural Networks (CNNs)

Brief look at Recurrent Neural Networks (RNNs)

Choosing the right network for your task

Module 7: Capstone Project – Train Your Own Neural Network

Select a project (image classification, sentiment prediction, or tabular regression)

Build, train, and test a neural network from end to end

Submit code, report, and model evaluation

Tools & Technologies Used:

Python

TensorFlow and Keras

PyTorch

Jupyter Notebook / Google Colab

Target Audience:

Beginners in deep learning and AI

Data science and computer science students

Developers looking to build intelligent systems

Professionals moving into neural model design

Global Learning Benefits:

Master the fundamental architecture behind deep learning

Understand how neural networks learn and generalize

Build hands-on projects with cutting-edge tools

Prepare for advanced AI courses in NLP, vision, and reinforcement learning

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