-
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…
-
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…
-
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,…
-
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…
-
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,…
-
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…
-
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…
-
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…
-
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…
-
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…
-
The Math Behind Every LLM, in Five Steps: Vectors to Cross-Entropy Loss
One continuous derivation connects vectors, matrices, gradients, and probability into the exact training objective behind every modern language model. Here’s the full arc, with the reasoning that forces each step.
-
Softmax and Cross-Entropy Explained: The Loss Function Behind Every LLM (Ep:02.04)
Softmax turns raw scores into a probability distribution; cross-entropy measures how wrong that distribution is. Module 2 of From Zero to Agents derives both from probability theory, proves the famous…
Search
Categories
- Artificial Intelligence (AI) (44)
- Learn FastAPI from Scratch (12)
- Shopify (1)
- Uncategorized (97)
- Versana Companion Plugin (7)
- Weebly (24)
- Weebly Apps (7)
- Wordpress (58)
- WordPress Block Theme Development (20)
- Zero to Automation Pro: The Complete n8n Developer Series (3)
Recent Posts
- Why Your Classifier Trains So Slowly at First (and MSE Might Be Why)
- How a Neural Network Actually Learns: From a Single Neuron to Backpropagation
- Why Cross-Entropy Beats MSE for Classification: The Gradient Math, Proven (Ep:03.06)
- Mini-Batch Gradient Descent Explained: Why Batching Is Just Matrix Multiplication (Ep:03.05)
- Backpropagation From Scratch: Deriving the Four Equations, Verified Against PyTorch (Ep:03.04)
Tags
AI course AI engineering angular attention mechanism backpropagation block theme BlockTheme c programming cross-entropy loss deep learning fastapi full site editing gradient descent gutenberg java learn AI from scratch learn angular learn html & html5 learn wordpress linear algebra machine learning math for machine learning mongodb n8n beginner guide n8n tutorial neural networks nginx NLP fundamentals nodejs react R programming versana versana companion plugin vps server website migration weebly app weebly apps weebly tutorials word embeddings wordpress blocks wordpress block theme wordpress plugin development wordpress plugins wordpress security wordpress tutorials