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The Transformer Block Explained: Multi-Head Attention, Residuals, and Layer Norm (Ep:04.03)
Residual connections fix vanishing gradients across depth the same way LSTM gating fixed them across time — both give the gradient a near-identity path. Module 4 of From Zero to…
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Learned Q, K, V Explained: What Makes Attention Actually Trainable (Ep:04.02)
Attention only becomes powerful once the query, key, and value projections are learned rather than reused from static embeddings. Module 4 of From Zero to Agents trains a real attention…
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Why Your Classifier Trains So Slowly at First (and MSE Might Be Why)
A classification model that’s confidently wrong early in training can get an almost nonexistent correction signal if you’re using the wrong loss function. Here’s the exact mechanism, measured, and why…
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Mini-Batch Gradient Descent Explained: Why Batching Is Just Matrix Multiplication (Ep:03.05)
Batched backpropagation isn’t a new algorithm — it’s the same four backprop equations with vectors replaced by matrices, where matrix multiplication sums over the batch automatically. Module 3 of From…
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Backpropagation From Scratch: Deriving the Four Equations, Verified Against PyTorch (Ep:03.04)
Backpropagation is four equations, applied layer by layer. Module 3 of From Zero to Agents derives all four from the chain rule, implements them in raw NumPy with no autograd,…
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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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The Vanishing Gradient Problem Explained: Why ReLU Replaced Sigmoid (Ep:03.02)
Sigmoid’s derivative maxes out at 0.25 and shrinks fast — multiply that through 15 layers and gradients vanish to nearly zero before reaching early layers. Module 3 of From Zero…
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Why Your Training Loss Exploded to NaN: The Math of Learning Rate, Not Guesswork
A loss curve that spikes to NaN mid-training isn’t random instability — it’s a provable consequence of crossing an exact convergence threshold. Here’s the derivation, and what it means for…
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From Zero to Agents: A Foundational AI/ML Course Built From First Principles (Episode 00.00)
Most “learn AI” content today teaches you to assemble — wire an LLM API into a framework, call it an agent, ship it. That’s a real and useful skill. It…
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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,…
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An Introduction To Deep Learning – AI
Deep learning is a branch of machine learning based on the artificial neural networks which automatically optimize their learned rules/ patterns when there’s a wrong prediction.
