✦Sparkl
innovator · learns

Neural Networks Under the Hood

Explore how artificial neurons learn patterns by tracing data flows, weights, and activation functions through multi-layer networks. Build intuition for backpropagation and training loops through hands-on computation.

20 modules·Difficulty: ★★★☆☆· 5 Free
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Modules

  • 1
    Free8 min
    The Artificial Neuron: Mimicking Biology
    Discover how a single neuron receives inputs, multiplies by weights, sums them, and fires an output 🔥
  • 2
    Free10 min
    Activation Functions: Gating the Signal
    Explore sigmoid, ReLU, and tanh functions that decide whether a neuron should activate or stay silent.
  • 3
    Free9 min
    Perceptrons and Linear Boundaries
    Learn why a single-layer perceptron can only draw straight lines to separate classes in data.
  • 4
    Free11 min
    Stacking Layers: Building Depth
    Understand how adding hidden layers lets networks learn complex, curved decision boundaries.
  • 5
    Free10 min
    Forward Propagation: Data Flows Forward
    Trace a numerical example as an input vector travels layer by layer to produce a final prediction.
  • 6
    Paid12 min
    Loss Functions: Measuring Mistakes
    Calculate mean squared error and cross-entropy to quantify how far predictions stray from true labels.
  • 7
    Paid11 min
    Gradients and the Chain Rule
    Discover how calculus chain rule connects each weight's tiny change to the overall loss.
  • 8
    Paid10 min
    Backpropagation: Learning in Reverse
    Follow error signals as they propagate backward, adjusting weights to reduce future mistakes.
  • 9
    Paid9 min
    Gradient Descent: The Optimization Engine
    Step weights downhill along the gradient slope to find minima in the loss landscape.
  • 10
    Paid11 min
    Learning Rate: Speed Versus Stability
    Experiment with large and small learning rates to see overshooting versus slow convergence.
  • 11
    Paid10 min
    Batching and Epochs: Training Dynamics
    Understand mini-batches and why multiple epochs help the network see patterns repeatedly.
  • 12
    Paid12 min
    Overfitting: When Models Memorize
    Identify when a network fits training data perfectly but fails on new examples.
  • 13
    Paid9 min
    Regularization Techniques: Taming Complexity
    Apply L2 penalties and dropout layers to prevent the network from over-relying on specific weights.
  • 14
    Paid11 min
    Validation and Test Sets: Honest Evaluation
    Split data into training, validation, and test partitions to measure true generalization ability.
  • 15
    Paid10 min
    Hyperparameters: Tuning the Knobs
    Adjust layer sizes, learning rates, and batch sizes to optimize network performance systematically.
  • 16
    Paid12 min
    Vanishing and Exploding Gradients
    Diagnose why very deep networks struggle when gradients shrink to zero or balloon to infinity.
  • 17
    Paid11 min
    Initialization Strategies: Starting Smart
    Compare random, Xavier, and He initialization to see how starting weights shape convergence speed.
  • 18
    Paid10 min
    Momentum and Adaptive Optimizers
    Enhance gradient descent with momentum, RMSProp, and Adam to accelerate training and smooth updates.
  • 19
    Paid12 min
    Capstone: Hand-Trace a 2-Layer Network
    Compute every weight update manually through one forward pass, loss calculation, and backward pass.
  • 20
    Final exam25 min
    Neural Networks Mastery Exam
    Demonstrate your understanding of neurons, layers, training loops, and optimization across ten questions.