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Machine Learning Foundations
- What a neural network isweighted sums, bent, stacked deep
- How models learnwrongness, measured, then walked downhill
- The kinds of learningwho provides the answers — and at what price
- Where data comes fromthe model is the dataset, rearranged
- Overfitting & generalizationmemorizing the test is not passing it
- Evaluating modelsthe score is a proxy; act like it
Large Language Models
- Transformers & attentionevery word looks at every other word
- Tokens & tokenizationthe atoms of model perception
- How LLMs are trainedfrom internet prediction to instruction following
- Fine-tuning in practicea thousand good examples and an adapter
- Context windows & memorythe model's whole world, measured in tokens
- Hallucinationfluent, confident, and wrong by design
- Scaling laws & small modelspredictable gains, surprising arrivals, shrinking costs
Prompt Engineering
- Anatomy of a promptinstructions early, context middle, format last
- Few-shot & chain of thoughtexamples teach, scratch tokens think
- Structured outputschemas, tools, and grammars over hope
- Why prompts failambiguous, buried, injected, and untested
AI Agents
- What makes an agenta model in a loop with consequences
- Tool use & function callingthe model asks, your code acts
- MCP: a standard for toolsone protocol between models and everything
- Computer usewhen the tool is the whole screen
- Multi-agent systemsa team of models, at a price
- Evaluating & containing agentstrust the state, not the summary
RAG & Embeddings
- Embeddingsmeaning as a point in space
- Vector searchfinding neighbors without checking everyone
- The RAG pipelinelook it up before answering
- RAG vs fine-tuningknowledge in context, behavior in weights
Generative Media
- Diffusion modelsstart from static, subtract the noise
- Image generation in practicedirecting a very literal artist
- Video, voice & musiccoherent for seconds, drifting by minutes
- Provenance & detectionassume detection fails, prove the real
Computer Vision
- How machines seefrom a grid of numbers to the idea of a cat
- Detection & segmentationnot just what — where, how many, which pixels
- Vision-language modelspictures and words, one shared space
- OCR & document AIturning paper into data you can trust
- Vision in the wildwhy the demo dies in the parking lot
Speech & Audio
- How machines hearsound as a picture of frequencies over time
- Speech to texttranscription got shockingly good — measure where it isn't
- Text to speechvoices are now synthesized, cloned, and directed
- The voice looplisten, think, speak — under 800 milliseconds, interruptible
- Audio beyond speechwho spoke, who is that, and what just broke
AI Safety & Alignment
- What alignment meansthe gap between what you optimized and what you meant
- How models are alignedpreferences in, constitution over, refusals shaped
- Jailbreaks & prompt injectionthe model does what the text says — whoever wrote it
- Evaluating safetya passed eval is a lower bound, not a verdict
- The long gameoverseeing systems smarter than the overseers
Reinforcement Learning
- Learning from consequencesno answer key — just a world that scores you
- Value functions & policy gradientstwo ways to turn experience into behavior
- Reward design & specification gamingthe agent optimizes the number, never the intention
- RL for language modelsthe quiet engine inside every frontier assistant
- When RL winssuperhuman at Go, struggling with a doorknob
Robotics & Embodied AI
- Why robots are hardchess fell in 1997; the doorknob still holds
- Sense, plan, actthe classical stack, and the learning that's eating it
- Learning to movedemonstrations, simulators, and internet-scale priors
- Manipulation & the humanoid bethands are the frontier; the body is a business case
- Robots at workwhere embodied AI already earns wages — and why pilots stall
AI Infrastructure & MLOps
- The hardware layermatrix multiplies, memory walls, and very expensive plumbing
- Training at scaleone loss curve, ten thousand chips, months of not crashing
- Serving & inferencethe meter runs per token — engineer accordingly
- The ML lifecyclethe model is 5% of the system; this is the other 95%
- LLMOpsprompts are config, evals are CI, tokens are COGS
Policy & Regulation
- The regulatory problemgoverning a technology that ships faster than law can read it
- The EU AI Actthe first comprehensive rulebook, tier by tier
- The patchworkfifty rulebooks, three philosophies, one internet
- Compliance in practicewhat the paperwork actually looks like from inside
- Open questionsthe debates that will define the next decade's rules
History of AI
- The founding dreama summer workshop names a field and mortgages its credibility
- Winters & thawstwice the field promised, twice the funding froze
- The quiet decadesstatistics, GPUs, and a million labeled images
- The deep-learning decadefrom AlexNet to ChatGPT in ten compounding years
- Reading the presentusing seventy years of pattern to see this moment clearly
The Lab — interactive
- Train a neural networkwatch a decision boundary get carved in real time
- Walk the loss landscapedrop a ball, pick a step size, watch it learn or explode
- Train a tokenizerwatch a vocabulary assemble itself out of characters
- Drive an attention headqueries, keys, and a softmax you can feel
- Sample from a language modelthe temperature knob, attached to an honest little model
- Teach an agentdraw a maze, watch Q-learning adapt — then betray it
- See your voiceyour microphone, rendered the way models hear
- Steer the denoiserthe guidance knob, from ignoring the prompt to frying it
- Hunt for neighborsexact retrieval vs the fast guess, recall meter running
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