MathIsimple
Machine Learning/Learning Center/Neural Networks

Neural Networks & Deep Learning

Journey through the evolution of neural networks, from simple perceptrons to cutting-edge deep learning architectures. Master the algorithms and techniques powering modern AI.

What You'll Learn

This comprehensive module takes you from the foundational concepts of artificial neurons to state-of-the-art deep learning systems. You'll explore the historical development, mathematical foundations, and practical applications of neural networks.

Core Concepts

  • •Neuron models and activation functions
  • •Perceptron learning and limitations
  • •Multi-layer network architectures
  • •Backpropagation algorithm mechanics

Advanced Topics

  • •Specialized network architectures (RBF, SOM, ART)
  • •Convolutional Neural Networks (CNNs)
  • •Modern optimization techniques
  • •Real-world applications and case studies

Learning Path

Module 1
Overview & History
Explore the fascinating evolution of neural networks from McCulloch-Pitts neurons to modern deep learning. Discover the three major phases of development, pioneering researchers, and breakthrough moments that shaped AI.

Key Topics:

Phase 1: Origins (1943-1969)Phase 2: Revival (1982-1990s)Phase 3: Deep Learning Era (2006-present)Pioneers: Hinton, LeCun, BengioModern Applications & Breakthroughs
Module 2
Neuron Models & Perceptron
Master the fundamentals of artificial neurons, from biological inspiration to the M-P model. Learn about activation functions, perceptron learning algorithms, and understand why single-layer networks have limitations.

Key Topics:

M-P Neuron ModelActivation Functions (Sigmoid, ReLU, Tanh)Perceptron Learning AlgorithmCredit Approval Classification ExampleXOR Problem & Limitations
Module 3
Multi-Layer Networks
Understand how adding hidden layers enables neural networks to solve complex problems. Learn about universal approximation, network architecture design, and practical strategies to prevent overfitting.

Key Topics:

Hidden Layers & ArchitectureUniversal Approximation TheoremHousing Price Prediction ExampleOverfitting Prevention StrategiesDropout & Batch NormalizationWeight Initialization Methods
Module 4
Backpropagation Algorithm
Deep dive into the algorithm that revolutionized neural network training. Master gradient descent, forward and backward passes, and learn optimization techniques used in modern deep learning.

Key Topics:

Chain Rule & Gradient DescentForward & Backward PassCustomer Churn Prediction ExampleVanishing/Exploding GradientsAdvanced Optimizers (SGD, Adam, RMSprop)Learning Rate Schedules
Module 5
Other Network Architectures
Explore specialized neural network architectures including RBF networks, Self-Organizing Maps, ART networks, and Restricted Boltzmann Machines. Learn when and how to apply each architecture.

Key Topics:

Radial Basis Function (RBF) NetworksSelf-Organizing Maps (SOM)Adaptive Resonance Theory (ART)Restricted Boltzmann Machines (RBM)Medical Diagnosis & Clustering ExamplesArchitecture Comparison Guide
Module 6
Deep Learning & CNNs
Enter the era of deep learning with Convolutional Neural Networks. Discover how CNNs revolutionized computer vision, learn about modern architectures, and explore cutting-edge applications in industry.

Key Topics:

Deep Learning RevolutionCNN Architecture (Conv, Pool, FC Layers)Image Classification & Object DetectionTransfer Learning & Pre-trained ModelsModern Architectures (ResNet, VGG)Real-World Case Studies