Clawdemy Lessons

Overview of supervised fine-tuning: distinguishing it from task fine-tuning, when to use SFT versus prompting, chat templates, the SFTTrainer, and LoRA.

What this fine-tuning lesson covers: prerequisites, the data collator, head-swap warning, TrainingArguments, the Trainer, and evaluation discipline.

Overview of the debugging lesson: what you'll learn, where it fits, prerequisites, and time, for reading tracebacks and asking for help effectively.

A guide to what this lesson covers: why data quality dominates LLM results, the Argilla curation workflow, dataset evaluation checks, and prerequisites.

What you will learn, prerequisites, and time for building a shareable Gradio demo and publishing it on Hugging Face Spaces, no frontend code.

A guided overview of the cost function: how it scores a network's wrongness, computing it by hand, and why learning is just minimizing that one score.

An overview of the backpropagation lesson: why brute force fails, the reframe to what each neuron wants, and how wishes roll backward to give the gradient.

Overview of the neuron computation: weighted sum, bias, and squish (sigmoid or ReLU), plus what weights and biases do and how to count a network's parameters.

A neural network as one function: brief overview of the forward pass, the f(x; w, b) split, and why parameter values, not structure, drive all behavior.

Overview of the cost landscape: the gradient, the negative gradient as the downhill compass, worked one and two-knob steps, and the local minimum caveat.

Overview of the neural network synthesis lesson: the whole picture in one breath, one full training step, what was deferred, and where to go next.

What this lesson on neurons and layers covers: activations, input and output layers, hidden layers, feedforward flow, plus prerequisites and timing.

A brief on gradient descent: the update rule, learning rate tradeoffs, the training loop, stochastic gradient descent, and what backpropagation will explain.

Backpropagation as the chain rule: a brief preview with learning outcomes, prerequisites, the worked chain, and where the vanishing gradient comes from.

Overview of the opening computer vision lesson: the semantic gap, the recognition challenges, why hand-written rules fail, and the data-driven approach.

What this opening lesson covers: the flip from rules to learning from data, the supervised and unsupervised families, and why models are judged on unseen data.

An orientation to support vector machines: what the maximum-margin idea and kernel trick teach, prerequisites, and the learning outcomes for this lesson.

Brief for the random forests lesson: what you will learn, how forests fit after decision trees, prerequisites, and read time before you start.

Overview of the logistic regression lesson: what you will learn, how it builds on linear regression and gradient descent, prerequisites, and time needed.

An orientation to k-means clustering: what you will learn, where unsupervised learning fits, prerequisites, and the time and difficulty to expect.

A study guide to gradient descent: what it is, where it fits after linear regression, and the skills you will have by the end.

Orientation for the hierarchical clustering lesson: what a dendrogram is, the prerequisite, learning outcomes, and read and practice time.

What the linear regression lesson covers, where it fits among classical machine learning methods, the prerequisites, and the time and difficulty.

Overview of the decision trees lesson: what you will learn, how it builds on logistic regression and sets up random forests, prerequisites, and time.

A quick orientation to boosting: what you'll learn, how it builds on random forests and gradient descent, prerequisites, and time and difficulty.