Clawdemy Lessons

Orientation for the vision-language lesson: what CLIP's joint embedding space unlocks, the prerequisites, the math level, plus read and practice time.

A walkthrough of the video architecture ladder, from the single-frame baseline through 3D convolutions and two-stream networks to video transformers.

Scope, prerequisites, and outcomes for the vision sequence-tools lesson: recurrence and attention, captioning, video, and the Vision Transformer.

An overview of self-supervised vision: why labels are scarce, the pretext-to-contrastive-to-masked-modeling arc, and the pre-train-then-transfer workflow.

What you'll learn from the neural networks and backprop lesson: why a ReLU adds capacity, how hidden layers learn features, and what backprop does.

A walkthrough of the training loop: the two standard losses, regularization, the gradient descent update rule, and mini-batch SGD, plus what to expect.

Orientation for the linear classifier lesson: what you will learn, how the score function s = W x + b fits the track, prerequisites, math needed, and timing.

What this lesson on deployed vision systems covers: the failure-mode catalog, bias as a measurable engineering property, and the trustworthiness gap.

Overview of the GANs and VAEs lesson: what you will learn, prerequisites, the reparameterization math, time and difficulty, and where it fits in this track.

What the diffusion lesson covers: the two-direction noising setup, the MSE training objective, inference speed-ups, and text-to-image conditioning.

A roadmap to going beyond image classification: what detection, segmentation, and visualization add, the IoU metric, prerequisites, and time and difficulty.

Overview of the convolution lesson: the conv operation, weight sharing, output-size and parameter formulas, plus prerequisites, math level, and time needed.

Preview of the CNN architectures lesson: the four landmarks you'll meet, the ImageNet numbers cited, prerequisites, and the training-at-scale subsection.

A roadmap to the 3D vision lesson: what you will learn, the depth cues and methods covered, prerequisites, the stereo-depth math, and timing.

What this t-SNE lesson covers: reading 2D cluster plots, what the method preserves and distorts, the perplexity dial, plus prerequisites and time to budget.

A map of the PCA lesson: what you'll learn about principal components, scree plots and loadings, plus prerequisites and where it fits in unsupervised learning.

Overview of the cross-validation lesson: learning outcomes, prerequisites, and time for train/validation/test splits, k-fold CV, and data-leakage traps.

Overview of the classification metrics lesson: what you'll learn about the confusion matrix, precision, recall, ROC and AUC, plus prerequisites and read time.

Overview of the bias-variance lesson: what you will learn, where it fits, the prerequisite to have first, and the time and difficulty before you start.

What the scaling-laws lesson covers: the power-law form, the Kaplan to Chinchilla shift, computing an optimal budget split, prerequisites, and read time.

Learning outcomes, prerequisites, and scope for the reasoning and RLVR lesson: the RLVR loop, GRPO, and how it caps the build-an-LLM pipeline.

Orientation for the post-training lesson: what SFT and preference tuning (RLHF, DPO) do, the prerequisites, and the technical-primer scope you can expect.

What the parallelism lesson covers: the three classic schemes, FSDP/ZeRO sharding, placement rules, and how 3D parallelism scales frontier training.

What this lesson covers: GPU kernels, why fusion is the single biggest performance lever, the Triton and XLA paths, and the FlashAttention case study.

Overview of what makes LLM inference expensive: the prefill/decode split, the KV cache, and the batching and quantization techniques, with prerequisites.