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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