The AI Training Coach
Understand machine learning training from the ground up – from neural networks to LoRA fine-tuning. 8 chapters, completely free.
Chapter 01
ML Basics
The basics: what is ML, how do neural networks and transformers work, how does an AI actually learn.
Chapter 02
Understanding Training
The training process in detail: the loop, loss functions, metrics, and the train/val/test split.
Chapter 03
Reading Training Curves
Reading loss curves correctly: recognizing good training, overfitting, underfitting, and unstable training.
Chapter 04
Diagnosis & Fixes
Diagnosis & fixes: concrete solutions for overfitting, underfitting, learning rate problems, and loss spikes.
Chapter 05
Hyperparameters
Hyperparameters in detail: learning rate, schedulers, batch size, optimizer comparison, and regularization.
Chapter 06
Fine-Tuning Methods
Fine-tuning methods compared: full fine-tuning, LoRA, QLoRA, other PEFT methods, and when to use what.
Chapter 07
Dataset Mastery
Dataset mastery: data quality, preprocessing, augmentation, and class balancing.
Chapter 08
Advanced Techniques
Advanced techniques: mixed precision, gradient checkpointing, early stopping, and ensembles.