Neural Network (NN) Notes for B.Tech 7th Sem PDF – 219+ Pages | Hybrid Handwritten + Typed | Easy Study Notes

Product Description

Get the most comprehensive NN notes covering everything from fundamentals to advanced algorithms. These notes simplify concepts like spatial filtering, frequency transforms, segmentation, restoration, and compression using diagrams and clear explanations.

Organized chapter-wise exactly as per updated university syllabi, this PDF ensures complete preparation for theory exams and viva.

What’s Inside the PDF? (Full Syllabus Coverage)

✔SECTION I: Overview of biological neurons:

• Structure of biological neuron
• Neurobiological analogy
• Biological neuron equivalencies to artificial neuron model
• Evolution of neural network

          Activation Functions:

• Threshold functions
• Signum function
• Sigmoid function
• Tan-hyperbolic function
• Stochastic function
• Ramp function
• Linear function
• Identity function

         ANN Architecture

• Feed forward network
• Feed backward network
• Single and multilayer network
• Fully recurrent network

 

✔ SECTION-II: McCulloch and Pits Neural Network (MCP Model)

• Architecture
• Solution of AND, OR function using MCP model

Image Restoration

• Image degradation and restoration process,
• Noise Models,
• Noise Filters,
• degradation function,
• Inverse Filtering,
• Homomorphism Filtering

Hebb Model: 

• Architecture, training and testing
• Hebb network for AND function

Perceptron Network:

• Architecture, training, Testing
• single and multi-output model
• Perceptron for AND function 
• Linear function
• application of linear model
• linear seperatablity
• solution of OR function using liner seperatablity model

✔ SECTION-III: Learning

• Supervised
• Unsupervised
• reinforcement learning
• Gradient Decent algorithm
• generalized delta learning rule
• Habbian learning
• Competitive learning

Back propogation Network: 

• Architecture, training and testing,

✔ SECTION-IV: Associative memory

• Auto associative and Hetro associative memory and their architecture
• training (insertion) and testing (Retrieval) algorithm using Hebb rule and Outer Product rule. 
• Storage capacity, 
• Testing of associative memory for missing and mistaken data, 
• Bidirectional memory.

Who Should Buy This PDF?

This notes package is ideal for:

• B.Tech (CSE / IT / ECE) Students
• BCA / MCA Students learning DIP
• Students preparing for semester exams
• GATE aspirants (for basic fundamentals)
• Anyone who wants easy explanations for Digital Image Processing

Why Students Trust Easy Study Notes?

• Clear handwriting
• Simple language
• Perfect exam format
• 100% syllabus covered
• Neatly scanned PDFs
• Easy for last-minute revision
• High exam retention value

Bonus Material

• Exam-ready questions
• Short revision notes
• Must-draw diagrams