what-is-neural-network.md

What is Neural Network? (In Simple Words)

I'll explain the detailed working of neural networks while keeping it understandable. Let's break it down step by step:

1. Basic Structure and Components:

   • Neurons (Nodes): These are the basic processing units that receive inputs, process them, and send outputs
   • Weights: Each connection between neurons has a weight (importance) attached to it
   • Bias: An additional value that helps adjust the output regardless of the input
   • Activation Functions: Mathematical functions that determine whether a neuron should be activated

2. Detailed Working Process:

   a) Input Processing:

   • Each input node receives a numerical value (like pixel values from an image)
   • These values are multiplied by weights (which start as random numbers)
   • A bias is added to this weighted sum

   For example:

   weighted_sum = (input1 × weight1) + (input2 × weight2) + bias

   b) Activation:

   • The weighted sum goes through an activation function
   • Common activation functions include:
     • ReLU (Rectified Linear Unit): Converts negative values to zero
     • Sigmoid: Squishes values between 0 and 1
     • Tanh: Squishes values between -1 and 1

   c) Forward Propagation:

   • The output from one layer becomes input for the next
   • Each subsequent layer processes information at a more abstract level
   • This continues until reaching the output layer

3. Learning Process (Backpropagation):

   • The network compares its output with the correct answer
   • It calculates the error (difference between prediction and actual)
   • The network adjusts weights and biases to minimize this error
   • This process happens backwards through the network

   Example:

   error = actual_output - predicted_output
   weight_adjustment = error × learning_rate × input_value

4. Training Process:

   • Data Preparation: Breaking down input data into suitable numerical format
   • Batch Processing: Training on small sets of data at a time
   • Epochs: Multiple passes through the entire dataset
   • Validation: Testing the network on unseen data to check performance

5. Optimization Techniques:

   • Learning Rate: Controls how much weights are adjusted
   • Dropout: Randomly turning off neurons to prevent overfitting
   • Batch Normalization: Normalizing the inputs of each layer
   • Momentum: Helping the network avoid local minima

Here's a practical example: Let's say we're teaching the network to recognize handwritten digits (0-9):

1. Input: 28×28 pixel image (784 input nodes)
2. Hidden Layer: Might have 128 neurons looking for basic patterns
3. Output: 10 nodes (one for each digit 0-9)
4. Training: Show thousands of examples with correct answers
5. Learning: Network adjusts weights to improve accuracy

The magic happens when the network discovers patterns on its own. For instance, it might learn that:

• Zeros typically have a continuous loop
• Ones are usually straight lines
• Sevens have a distinctive angle
• These patterns emerge without explicitly programming them

Understanding this process helps explain why neural networks need:

• Large amounts of training data
• Significant computational power
• Careful tuning of hyperparameters (learning rate, network architecture, etc.)