🧠 Neural Networks

Master artificial neural networks, backpropagation, and deep learning fundamentals for intelligent systems

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Neural Networks Curriculum

12
Core Units
~85
Key Concepts
15+
Architectures
35+
Practical Examples
1

Introduction to Neural Networks

Understand the biological inspiration and fundamental concepts of artificial neural networks.

  • Biological neural networks
  • Artificial neuron model
  • Neural network history
  • Key advantages
  • Common applications
  • Neural network types
  • Learning paradigms
  • Modern deep learning
2

Perceptron and Linear Models

Learn the foundation of neural networks with the perceptron and linear classification.

  • Perceptron algorithm
  • Linear separability
  • Weight updates
  • Perceptron limitations
  • Multi-layer necessity
  • XOR problem
  • Linear vs non-linear
  • Historical significance
3

Activation Functions

Master the critical role of activation functions in neural network learning.

  • Purpose of activation functions
  • Sigmoid function
  • Hyperbolic tangent (tanh)
  • ReLU and variants
  • Softmax for classification
  • Activation function properties
  • Vanishing gradient problem
  • Choosing activation functions
4

Multi-Layer Perceptrons

Build feedforward neural networks with multiple layers for complex pattern recognition.

  • Multi-layer architecture
  • Hidden layers
  • Universal approximation
  • Network topology
  • Feedforward computation
  • Layer interactions
  • Depth vs width
  • Network design principles
5

Forward Propagation

Understand how information flows through neural networks during prediction.

  • Input to output flow
  • Layer-by-layer computation
  • Matrix operations
  • Weighted sums
  • Activation application
  • Output generation
  • Computational efficiency
  • Vectorized implementation
6

Loss Functions

Learn how to measure and optimize neural network performance using loss functions.

  • Purpose of loss functions
  • Mean squared error
  • Cross-entropy loss
  • Binary vs categorical
  • Sparse categorical
  • Custom loss functions
  • Loss function properties
  • Choosing appropriate loss
7

Backpropagation

Master the fundamental algorithm that enables neural network learning.

  • Gradient descent basics
  • Chain rule of calculus
  • Error backpropagation
  • Gradient computation
  • Weight updates
  • Learning rate
  • Computational graphs
  • Automatic differentiation
8

Training Neural Networks

Learn practical techniques for effectively training neural networks.

  • Training process
  • Batch vs online learning
  • Mini-batch gradient descent
  • Epochs and iterations
  • Learning rate scheduling
  • Weight initialization
  • Convergence monitoring
  • Training best practices
9

Regularization Techniques

Prevent overfitting and improve generalization with regularization methods.

  • Overfitting in neural networks
  • L1 and L2 regularization
  • Dropout technique
  • Early stopping
  • Batch normalization
  • Data augmentation
  • Weight decay
  • Regularization strategies
10

Optimization Algorithms

Explore advanced optimization techniques for efficient neural network training.

  • Gradient descent variants
  • Momentum optimization
  • AdaGrad algorithm
  • RMSprop optimization
  • Adam optimizer
  • Learning rate adaptation
  • Second-order methods
  • Optimizer selection
11

Neural Network Architectures

Explore different neural network architectures for various problem types.

  • Feedforward networks
  • Convolutional networks
  • Recurrent networks
  • Autoencoder networks
  • Residual networks
  • Attention mechanisms
  • Transformer architecture
  • Architecture design
12

Implementation and Practice

Build neural networks from scratch and with popular frameworks.

  • NumPy implementation
  • TensorFlow basics
  • PyTorch fundamentals
  • Keras high-level API
  • Model building workflow
  • Debugging neural networks
  • Performance optimization
  • Real-world applications

Unit 1: Introduction to Neural Networks

Understand the biological inspiration and fundamental concepts of artificial neural networks.

Biological Neural Networks

Learn how biological neurons inspired the development of artificial neural networks.

Biology Inspiration Neurons
Biological neurons receive signals through dendrites, process them in the cell body, and transmit outputs through axons. This inspired the artificial neuron model with inputs, processing, and outputs.

Artificial Neuron Model

Understand the mathematical model that simulates biological neuron behavior.

y = f(∑(wᵢxᵢ) + b)
where f is activation function, w are weights, x are inputs, b is bias
import numpy as np

class ArtificialNeuron:
  def __init__(self, num_inputs):
    """Initialize neuron with random weights and bias"""
    self.weights = np.random.randn(num_inputs) * 0.01
    self.bias = 0.0
  
  def sigmoid(self, x):
    """Sigmoid activation function"""
    return 1 / (1 + np.exp(-np.clip(x, -500, 500)))
  
  def forward(self, inputs):
    """Forward pass: compute output"""
    # Weighted sum of inputs
    weighted_sum = np.dot(inputs, self.weights) + self.bias
    
    # Apply activation function
    output = self.sigmoid(weighted_sum)
    
    return output

# Demonstrate artificial neuron
def demonstrate_neuron():
  """Show how an artificial neuron works"""
  
  print("=== ARTIFICIAL NEURON DEMONSTRATION ===")
  
  # Create neuron with 3 inputs
  neuron = ArtificialNeuron(num_inputs=3)
  
  # Show initial weights and bias
  print(f"Initial weights: {neuron.weights}")
  print(f"Initial bias: {neuron.bias}")
  
  # Test inputs
  test_inputs = [
    [1.0, 0.5, -1.0],
    [0.0, 1.0, 1.0],
    [-1.0, 0.0, 0.5]
  ]
  
  print("\\n=== NEURON RESPONSES ===")
  for i, inputs in enumerate(test_inputs):
    output = neuron.forward(np.array(inputs))
    weighted_sum = np.dot(inputs, neuron.weights) + neuron.bias
    
    print(f"\\nInput {i+1}: {inputs}")
    print(f" Weighted sum: {weighted_sum:.3f}")
    print(f" Output (after sigmoid): {output:.3f}")

demonstrate_neuron()

print("\\n=== NEURON COMPONENTS ===")
components = {
  "Inputs (x)": "Data features fed to the neuron",
  "Weights (w)": "Learned parameters controlling input importance",
  "Bias (b)": "Learned offset parameter",
  "Weighted Sum": "Linear combination: w₁x₁ + w₂x₂ + ... + b",
  "Activation": "Non-linear function applied to weighted sum",
  "Output (y)": "Final neuron response"
}

for component, description in components.items():
  print(f"{component}: {description}")

Neural Network History

Explore the key milestones in neural network development from the 1940s to today.

1943: McCulloch-Pitts neuron model
1957: Perceptron algorithm
1969: Perceptron limitations identified
1986: Backpropagation algorithm
2006: Deep learning renaissance
2012: AlexNet breakthrough
# Neural network history timeline

nn_history = {
  1943: {
    "Event": "McCulloch-Pitts Neuron",
    "Description": "First mathematical model of artificial neuron",
    "Impact": "Foundation of neural computation"
  },
  1957: {
    "Event": "Perceptron Algorithm",
    "Description": "Frank Rosenblatt's learning algorithm",
    "Impact": "First trainable neural network"
  },
  1969: {
    "Event": "Perceptrons Book",
    "Description": "Minsky & Papert show perceptron limitations",
    "Impact": "First AI winter begins"
  },
  1986: {
    "Event": "Backpropagation",
    "Description": "Rumelhart, Hinton & Williams formalize backprop",
    "Impact