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How a Neural Network Actually Learns: From a Single Neuron to Backpropagation
A single artificial neuron can’t solve XOR. Here’s the full, provable chain of fixes — nonlinearity, activation functions, initialization, backpropagation, batching, and the right loss function — that turns that…
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Why Cross-Entropy Beats MSE for Classification: The Gradient Math, Proven (Ep:03.06)
A confidently wrong sigmoid+MSE network gets a weaker correction signal exactly when it needs a stronger one. Module 3 of From Zero to Agents derives why softmax+cross-entropy avoids this pathology,…
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Why Weight Initialization Matters: Xavier and He Init, Derived From Scratch (Ep:03.03)
The wrong starting weights can make a deep network’s activations vanish to zero or explode to millions before training even begins. Module 3 of From Zero to Agents derives Xavier…
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How Multi-Layer Neural Networks Solve XOR (and Why Nonlinearity Is Non-Negotiable) (Ep:03.01)
Stacking two perceptrons solves the problem one perceptron provably can’t. Module 3 of From Zero to Agents hand-builds a working XOR network, proves why linear layers alone can never work…
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The Perceptron and the XOR Problem: Why Single Neurons Aren’t Enough (Ep:03.00)
In 1969, Minsky and Papert proved a single perceptron can never learn XOR. Module 3 of From Zero to Agents derives that proof from scratch, trains a real perceptron in…
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Review Rating Classification System Using Artificial Intelligence
Our research article titled “Multi Class Review Rating Classification Using Deep Recurrent Neural Networks” was published in an international journal “Neural Processing Letters” on 15 October 2019. In this tutorial,…
