To drive the concept home, Professor Kumar showed a simple demonstration using a neural network implemented in Python. The network was trained to recognize handwritten digits (0-9) using the popular MNIST dataset.
: Many legitimate academic portals offer accompanying MATLAB/Python code repositories and lecture slides alongside the text.
Each LO maps to a cognitive level (Remember → Understand → Apply → Analyze → Evaluate → Create). For instance, (“ Analyze the effect of sequence length on gradient stability in RNNs ”) requires analysis and can be assessed through a written report. Neural Networks A Classroom Approach By Satish Kumar.pdf
Understanding how a single neuron learns is crucial before building massive networks. This section covers:
Example (Adam update): m_t = β1 m_t-1 + (1-β1) g_t; v_t = β2 v_t-1 + (1-β2) g_t^2; bias-corrected and update weights. To drive the concept home, Professor Kumar showed
: Buy the physical book if available in your region; borrow a digital copy through official channels; and most importantly, keep a notebook and a pencil beside your screen .
This section forms the core mathematical engine of the textbook. Each LO maps to a cognitive level (Remember
Neural networks rely heavily on linear algebra, calculus, and probability. Kumar handles this by presenting the necessary mathematics contextually. The book excels in its explanation of , providing clear derivations for the Hebbian rule, the Perceptron learning rule, and the Delta rule. By breaking down the derivations line-by-line, the text removes the intimidation factor often associated with the math behind backpropagation.