Ultimate B.Tech 7th Semester Neural Network Notes PDF – 250+ Pages | Hybrid Notes | Easy Study Notes

Description

Product Description

Neural Network requires clear conceptual understanding — and these notes deliver exactly that. With structured explanations, labelled illustrations, and exam-ready summaries, the PDF ensures complete preparation.

Great for students under AKTU, VTU, RGPV, JNTU, GTU, PTU and more.

📂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 NN
• Students preparing for semester exams
• GATE aspirants (for basic fundamentals)
• Anyone who wants easy explanations for Neural Network

Why Students Trust Easy Study Notes?

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

Bonus Material

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