Issue #49 • Feb 18, 2026 Three Ways to Customize LLMs: Prompting, RAG, and Fine-Tuning Three approaches to customize LLMs: implementation guide with code examples. Read issue
Issue #48 • Feb 12, 2026 What is RAG? Building Production AI Without Training Models Production AI without model training: How RAG works with code examples. Read issue
Issue #47 • Feb 04, 2026 Embeddings & Vector Spaces: How AI Understands Meaning How AI turns words into meaning using vectors Read issue
Issue #46 • Jan 30, 2026 Transformers: The Architecture That Changed Everything The architecture behind ChatGPT, Claude, and every major AI breakthrough - explained simply. Read issue
Issue #45 • Jan 23, 2026 Attention Mechanisms: Teaching Neural Networks Where to Look Weighted embeddings and why attention replaced RNNs for modern AI. Read issue
Issue #44 • Jan 14, 2026 Recurrent Neural Networks: Processing Sequences and Time How RNNs process sequences through hidden state loops and LSTM gates. Read issue
Issue #43 • Jan 08, 2026 Convolutional Neural Networks: How AI Sees Images From edge detection to face recognition: How CNNs use sliding filters and parameter sharing to understand images with 1000x fewer parameters. Read issue
Issue #42 • Jan 06, 2026 AI in 2026: The 'Show Me the Money' Year Week 1 of 2026: ROI pressure, quantum bets, transformer plateau, and physical AI Read issue
Issue #41 • Dec 31, 2025 What Are Tensors? (And Why Modern AI Needs Them) The multi-dimensional data structures that power modern AI architectures. Read issue
Issue #40 • Dec 29, 2025 Training Neural Networks: The Complete Learning Loop The complete 7-step training loop: epochs, batches, overfitting, and when to stop. Read issue
Issue #39 • Dec 20, 2025 How Neural Networks Actually Learn (Gradient Descent) What gradient descent is, how it uses gradients to update weights, and why it's the optimization algorithm that makes neural network learning possible Read issue
Issue #38 • Dec 12, 2025 Loss Functions - How Neural Networks Measure Their Mistakes What loss functions are, why neural networks need them, and how to choose between MSE and cross-entropy for your problem Read issue
Issue #37 • Dec 04, 2025 Activation Functions: Why Neural Networks Need Them Understand why neural networks need activation functions and how ReLU, Sigmoid, and Tanh introduce the non-linearity that makes deep learning work. Read issue
Issue #36 • Nov 29, 2025 How Neural Networks Learn (Forward & Backward Propagation) Learn how neural networks learn through forward and backward propagation. Understand how data flows forward, errors are calculated, and weights get adjusted to improve predictions. Read issue
Issue #35 • Nov 22, 2025 Inside a Neural Network: Neurons, Weights, and Biases Explained The building blocks of neural networks. How neurons, weights, and biases work together to help AI learn patterns from data. Read issue
