Agentic AI, Model Training, and CUDA

Define a Python class with initialized state and methods.

Create NumPy arrays and apply indexing, reshaping, vectorized operations, and broadcasting.

Load, transform, and save tabular data with pandas.

Serialize and deserialize structured data using JSON.

Send an HTTP request from Python and interpret a JSON response.

Validate function inputs and handle expected failures with Python exceptions.

Identify the dimensions and meaning of scalars, vectors, matrices, and higher-rank tensors.

Compute dot products and matrix products and verify that their dimensions are compatible.

Calculate a neuron's weighted sum and apply a nonlinear activation function.

Compute the derivative of a simple single-variable function and interpret its slope.

Apply the chain rule to calculate gradients in a small computational graph.

Summarize a dataset using means, variances, and simple empirical probabilities.

Compute softmax probabilities and cross-entropy loss for a small classification example.

Implement gradient descent to minimize a one-parameter loss function.

Frame a concrete prediction problem by identifying inputs, targets, and a measurable objective.

Partition labeled data into training, validation, and test sets without data leakage.

Transform raw features into numerical inputs suitable for a machine learning model.

Fit a baseline regression or classification model with scikit-learn.

Calculate and interpret appropriate classification or regression evaluation metrics.

Diagnose underfitting and overfitting from training and validation results.

Create PyTorch tensors and perform shape-safe tensor operations.

Use automatic differentiation to calculate parameter gradients.

Define a multilayer neural network by subclassing PyTorch's nn.Module.

Select an output layer and loss function that match a regression or classification task.

Explain why GPUs accelerate parallel tensor workloads using cores, streaming multiprocessors, and SIMT execution.

Select a CUDA-enabled Colab runtime and verify GPU availability from Python and PyTorch.

Move models and tensors between CPU and GPU without device-mismatch errors.

Package examples into a Dataset and iterate over mini-batches with a DataLoader.

Train and evaluate a small neural classifier using a mini-batch PyTorch loop.

Apply automatic mixed-precision training with gradient scaling.

Diagnose GPU out-of-memory failures and unintended PyTorch tensor retention, and select an appropriate remediation.

Compare data, tensor/model, and pipeline parallelism and select an appropriate multi-GPU strategy for a stated workload.

Encode and decode text with a subword tokenizer and inspect the resulting token sequence.

Map token IDs to learned embeddings and add positional information.

Compute scaled dot-product attention for a small set of query, key, and value vectors.

Apply a causal attention mask to prevent access to future tokens.

Explain how multiple attention heads capture different token relationships.

Implement a transformer block with attention, a feed-forward network, residual connections, and layer normalization.

Stack transformer blocks to construct a tiny autoregressive language model in PyTorch.

Calculate a transformer's parameter count and estimate the memory required for its parameters.

Train a subword tokenizer on a small text corpus and inspect the resulting vocabulary and tokenization.

Convert a text corpus into training and validation batches with next-token labels.

Configure an optimizer, learning-rate schedule, and gradient clipping for stable language-model training.

Train a tiny autoregressive transformer on a hosted GPU and track its loss.

Save and resume model, optimizer, and training state from a checkpoint.

Calculate validation loss and perplexity for a language model.

Generate text with greedy decoding, temperature, top-k sampling, and top-p sampling.

Diagnose unstable or overfit language-model training from loss curves and generated samples.

Define the target domain behavior and decide whether prompting, retrieval, continued pretraining, or supervised fine-tuning best addresses the gap.

Select an open pretrained model by checking its license, task fit, context length, tokenizer, and hosted-GPU memory requirements.

Clean, deduplicate, and split a small domain dataset without leaking evaluation examples into training.

Format domain examples with the model's chat template and mask tokens that should not contribute to the training loss.

Establish an untuned baseline on a held-out set of representative domain prompts.

Explain how LoRA and QLoRA reduce trainable parameters and GPU memory requirements.

Fine-tune a compact open model on domain data with QLoRA in Colab.

Compare the base and fine-tuned models on held-out domain tasks and identify regressions outside the target behavior.

Distinguish CUDA hardware, drivers, runtimes, kernels, and framework-level GPU operations.

Map array elements to CUDA threads using grid, block, and thread indices.

Read a simple CUDA C kernel and identify its pointers, indices, and launch parameters.

Write and launch an elementwise Numba CUDA kernel from Python in Colab.

Add boundary checks and validate CUDA kernel output against a CPU reference.

Explain how registers, shared memory, caches, global memory, and coalesced access affect kernel performance.

Use CUDA events, warm-up runs, and synchronization to benchmark a kernel correctly.

Compare a custom CUDA kernel with an optimized PyTorch operation and decide whether customization is justified.

Call the Anthropic API from Colab while keeping credentials out of notebook source code.

Construct system and user messages that clearly specify an automation task and its constraints.

Parse a model response and validate structured data against a defined JSON schema.

Define a tool with a focused purpose, typed inputs, and an unambiguous result.

Execute a model-requested tool and return its result in the expected conversation format.

Represent an agent's task progress and tool results as explicit state.

Implement a bounded model-tool interaction loop with an explicit termination condition.

Route requests among tools while restricting unavailable or inappropriate actions.

Specify a bounded automation use case with permitted actions, a narrowly defined domain-model role, failure conditions, and measurable success criteria.

Implement mock task-management, calendar, and messaging tools with validated inputs and structured outputs.

Package the domain-adapted model as a narrowly scoped inference tool with a documented input-output contract.

Connect the Anthropic model, explicit agent state, domain-model component, and automation tools into an end-to-end workflow.

Add timeouts, retries, and idempotency protections to external tool calls.

Require user confirmation before the agent performs a consequential simulated action.

Identify prompt-injection risks in untrusted tool data and isolate that data from control instructions.

Evaluate an agent with a repeatable suite of success, safety, and efficiency tests.

Map the AI value chain and explain how GPU, cloud, model, infrastructure, and application companies create customer value, capture profit, and transfer value between layers.

Explain how accelerator supply, memory bandwidth, networking, energy, utilization, depreciation, and purchasing scale shape GPU-provider and cloud economics.

Estimate training and inference unit costs and calculate the usage level at which a token-priced API or self-hosted model becomes more economical.

Calculate revenue, gross profit, gross margin, contribution margin, and capital intensity for simplified AI business models at different stack layers.

Assess the durability of value capture using retention, pricing power, distribution, proprietary data, switching costs, supplier dependence, and likely value migration.

Build a comparable-company valuation using revenue or ARR multiples adjusted for growth, margins, retention, and capital intensity.

Construct a venture-return scenario using entry valuation, ownership, dilution, and exit assumptions to calculate MOIC and IRR.

Prepare a VC-style investment memo with a valuation range, bull-base-bear return scenarios, key diligence questions, and the principal technical and commercial risks.