Neural Networks A Classroom Approach By Satish Kumar.pdf -

Neural Networks: A Classroom Approach – A Comprehensive Review and Teaching Guide
Author: Satish Kumar
Edition: 2023 (PDF edition)

Chapter 13: Generative Models – GANs & VAEs

• GAN Anatomy: Generator, discriminator, adversarial loss, mode collapse.
• VAE: Variational lower bound, reparameterization trick.
• Hands‑On: Train a DCGAN to generate handwritten digits; experiment with latent‑space interpolations.

2.4 Variations and Improvements

• Quickprop, RPROP.
• Batch vs. stochastic vs. mini-batch gradient descent.
• Regularization: weight decay, early stopping.
• Dropout (though more recent, some editions include it).

7.1 Binary Classification with a Small MLP (pseudocode)

• Input: features x ∈ R^d, label y ∈ 0,1.
• Model: Dense(d→h, ReLU) → Dense(h→1, Sigmoid).
• Loss: binary cross-entropy.
• Train: mini-batch SGD/Adam, monitor validation AUC.

Pseudocode:

for epoch in range(E):
  for batch_x, batch_y in loader:
    logits = model(batch_x)
    loss = BCE(logits, batch_y)
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

Chapter 15: Emerging Directions & AI Ethics

• Trends: Self‑supervised learning, foundation models, neuromorphic hardware.
• Discussion: AI governance, data privacy, model provenance.
• Reflection Prompt: Write a 500‑word essay on the societal impact of ubiquitous deep‑learning systems.