🟣 ML + AI  ·  Lesson 63

Neural Network Basics

Neural Network Basics क्या है?

Neural Network Basics ka matlab hai: Neural networks are inspired by the human brain and are used for complex tasks like image, speech and language processing. Simple words me, ye topic practical Python programs likhne me direct use hota hai.

Is topic ko sirf definition ke liye nahi, balki understanding neurons and layers jaise real examples ke liye practice karein.

यह क्यों सीखना जरूरी है?

  • Ye understanding neurons and layers me kaam aata hai.
  • Ye intro to deep learning se bhi connected hai.
  • Isse aap code ka output aur errors better samajh paate hain.

Important Terms

TermMeaning
neuronBasic unit of a neural network.
layerGroup of neurons in a neural network.
activationFunction that decides neuron output.
weightsweights is an important term in this topic.
trainingProcess where a model learns from data.

Syntax / Basic Pattern

Basic idea: pehle data तैयार करें, phir Python logic apply करें, aur finally result display करें.

Basic Pattern
inputs = [2, 3]
weights = [0.4, 0.6]
bias = 1
output = inputs[0] * weights[0] + inputs[1] * weights[1] + bias
print("Neuron output:", output)

Complete Example Program

Python – neural-network-basics.py
# Simple neuron calculation
inputs = [2, 3]
weights = [0.4, 0.6]
bias = 1

output = inputs[0] * weights[0] + inputs[1] * weights[1] + bias
print("Neuron output:", output)

Expected Output

Neuron output: 3.5999999999999996

Program Explanation

  • inputs = [2, 3] stores a value in inputs.
  • weights = [0.4, 0.6] stores a value in weights.
  • bias = 1 stores a value in bias.
  • output = inputs[0] * weights[0] + inputs[1] * weights[1] + bias stores a value in output.
  • print("Neuron output:", output) displays information or calculated result on the screen.

Practical Uses

  • Understanding neurons and layers.
  • Intro to deep learning.
  • Building simple prediction models.

Common Mistakes

  • Training and testing the model on the same data.
  • Using an algorithm without understanding the input features.
  • Reporting only accuracy without checking actual mistakes and limitations.

Practice Tasks

  1. Program ko neural-network-basics.py file me type karke run karein.
  2. Values change karke output compare karein.
  3. understanding neurons and layers par ek छोटा example banayen.
  4. Logic ko apne words me 5 lines me likhein.

सारांश

Neural Network Basics ko tab complete maanenge jab aap iska meaning, example, output aur practical use clearly explain kar saken.

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