Description
Product Description
Understanding Neural Network can be challenging due to complex algorithms, mathematical concepts, and detailed diagrams. To make it easier for students, Easy Study Notes brings you the most complete, clean, and exam-ready Neural Network Notes PDF for B.Tech 7th Semester.
These notes are crafted using a hybrid format:
✔ Neat handwritten explanations
✔ Cleanly typed chapter-wise summaries
✔ Well-labeled diagrams
✔ Flowcharts
✔ Algorithm steps
✔ Important definitions and formulas
Designed strictly as per the latest university curriculum followed by AKTU, RGPV, VTU, JNTU, MAKAUT, GTU, PTU, BPUT, and top Indian engineering universities.
Whether you are preparing for theory exams, class tests, practicals, or assignments, this PDF is your perfect study companion.
📂 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
Bonus Content Included
Along with the main notes, you also get:
Unit-wise Important Questions
High-scoring Diagrams
One-Page Short Notes for Quick Revision
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
Neatly scanned PDFs
Easy for last-minute r
evision
High exam retention value
📥 Download Your PDF & Start Scoring Higher in Exams!
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