<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>AI/ML Field Guide — all updates</title><description>Structured lessons, deep-dive topic hubs, platform and model references, and daily AI and machine learning news written in plain English. From first classifier to research frontier.</description><link>https://www.aimlfieldguide.com/</link><language>en-gb</language><item><title>Lesson: What is machine learning?</title><link>https://www.aimlfieldguide.com/learn/beginner/01-what-is-machine-learning/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/learn/beginner/01-what-is-machine-learning/</guid><description>Programs whose rules are fitted to data rather than written by hand, the train and predict loop, the vocabulary you will use for the rest of the track, and when machine learning is the wrong tool</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Lesson: Data is the model: datasets, splits and leakage</title><link>https://www.aimlfieldguide.com/learn/beginner/02-data-is-the-model/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/learn/beginner/02-data-is-the-model/</guid><description>Build a small synthetic dataset with pandas, split it into train, validation and test sets, then plant a leaky feature and watch the validation score lie</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Lesson: Linear and logistic regression from scratch</title><link>https://www.aimlfieldguide.com/learn/beginner/03-linear-models-from-scratch/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/learn/beginner/03-linear-models-from-scratch/</guid><description>Least squares solved in closed form with numpy, then logistic regression fitted by gradient descent with the sigmoid and log loss, both checked against scikit-learn</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Lesson: The training loop: loss, gradients, learning rate</title><link>https://www.aimlfieldguide.com/learn/beginner/04-training-loop-and-loss/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/learn/beginner/04-training-loop-and-loss/</guid><description>The four-step loop that trains almost every model, the gradient checked by hand and by finite differences, and a learning-rate sweep that shows slow, right and divergent on one chart</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Lesson: Decision trees and gradient boosting</title><link>https://www.aimlfieldguide.com/learn/beginner/05-trees-and-gradient-boosting/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/learn/beginner/05-trees-and-gradient-boosting/</guid><description>How a decision tree chooses its splits, what depth does to training and held-out accuracy, how gradient boosting stacks small trees into a strong model, and why feature importance needs care</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Lesson: Evaluation that does not lie</title><link>https://www.aimlfieldguide.com/learn/beginner/06-evaluation-that-does-not-lie/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/learn/beginner/06-evaluation-that-does-not-lie/</guid><description>Why accuracy misleads on imbalanced data, what precision, recall, F1 and ROC-AUC measure, how stratified cross-validation and a dummy baseline keep you honest, and how a bootstrap puts an interval on a score</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Lesson: Your first neural network in PyTorch</title><link>https://www.aimlfieldguide.com/learn/beginner/07-your-first-neural-network/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/learn/beginner/07-your-first-neural-network/</guid><description>Tensors, autograd, nn.Module and the training loop, applied to a small multilayer perceptron that reads 8x8 digit images on a laptop CPU in a few seconds</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Lesson: Embeddings and similarity search</title><link>https://www.aimlfieldguide.com/learn/beginner/08-embeddings-and-similarity/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/learn/beginner/08-embeddings-and-similarity/</guid><description>What an embedding vector is, cosine similarity worked by hand, a 22 M parameter sentence encoder run locally on twenty sentences, nearest-neighbour search with numpy, and why it beats a keyword index on meaning</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Lesson: Using an LLM well: prompts, structure and limits</title><link>https://www.aimlfieldguide.com/learn/beginner/09-using-an-llm-well/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/learn/beginner/09-using-an-llm-well/</guid><description>What tokens and a context window are, how system and user messages differ, how to ask for JSON and validate it, few-shot examples, and the limits every model shares, with code for a local model and a hosted API</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Lesson: From notebook to service: packaging, a FastAPI endpoint, a smoke test</title><link>https://www.aimlfieldguide.com/learn/beginner/10-from-notebook-to-service/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/learn/beginner/10-from-notebook-to-service/</guid><description>Train a small scikit-learn pipeline, save it with joblib, wrap it in a FastAPI endpoint with validated inputs, test the endpoint in-process without a server, and lay the project out so someone else can run it</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Lesson: Backpropagation by hand, then in autograd</title><link>https://www.aimlfieldguide.com/learn/intermediate/01-backpropagation-by-hand/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/learn/intermediate/01-backpropagation-by-hand/</guid><description>Work the chain rule through a two-layer network with real numbers, code the backward pass in numpy, check it with finite differences, then watch PyTorch autograd produce the same gradients</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Project: An MNIST-class CNN in PyTorch on CPU</title><link>https://www.aimlfieldguide.com/projects/mnist-cnn-pytorch/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/projects/mnist-cnn-pytorch/</guid><description>Train a two-layer convolutional network on 16x16 digit images in under a second on a laptop CPU, read the loss curve, print a confusion matrix, and find out which digits it gets wrong.</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Project: A tabular classifier with a FastAPI endpoint</title><link>https://www.aimlfieldguide.com/projects/tabular-classifier-fastapi/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/projects/tabular-classifier-fastapi/</guid><description>Train a scikit-learn pipeline on the breast cancer dataset, save it with joblib, serve it from a FastAPI app with /predict and /health, and test the app in-process before you ever start a server.</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Project: Fine-tune a small model with LoRA on Apple Silicon in under 15 minutes</title><link>https://www.aimlfieldguide.com/projects/lora-fine-tune-apple-silicon/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/projects/lora-fine-tune-apple-silicon/</guid><description>Teach SmolLM2-135M a date-format conversion it cannot do out of the box, with a rank-8 LoRA adapter trained for 60 steps on MPS or CPU, then save, reload and verify the adapter.</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Project: A tiny RAG system over a folder of text with local embeddings</title><link>https://www.aimlfieldguide.com/projects/tiny-rag-local-embeddings/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/projects/tiny-rag-local-embeddings/</guid><description>Chunk a folder of notes, embed the chunks with all-MiniLM-L6-v2 on your CPU, retrieve with a cosine index in plain numpy, then hand the top chunks to a local model through Ollama for a cited answer.</description><pubDate>Fri, 13 Nov 2026 00:00:00 GMT</pubDate></item><item><title>vLLM v0.33.0 ships with 927 commits as v0.34.0 release candidates land</title><link>https://www.aimlfieldguide.com/news/2026-11-12-vllm-v0-33-0-ships-with-927-commits-as-v0-34-0-release/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/news/2026-11-12-vllm-v0-33-0-ships-with-927-commits-as-v0-34-0-release/</guid><description>vLLM tagged v0.33.0 on 2 November 2026 with shorter Docker image names, then published two v0.34.0 release candidates within ten days.</description><pubDate>Thu, 12 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Anthropic launches IPO roadshow, offering 825 million Class A shares at $70 to $78 on Nasdaq</title><link>https://www.aimlfieldguide.com/news/2026-11-10-anthropic-ipo-roadshow/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/news/2026-11-10-anthropic-ipo-roadshow/</guid><description>The proposed listing under the ticker ANT follows the S-1 filed on 26 October. Anthropic itself is selling 801 million shares, with no proceeds, pricing date or closing date yet stated.</description><pubDate>Tue, 10 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Hugging Face adds a decision pipeline for models that score options instead of writing text</title><link>https://www.aimlfieldguide.com/news/2026-11-10-hugging-face-adds-a-decision-pipeline-for-models-that-score/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/news/2026-11-10-hugging-face-adds-a-decision-pipeline-for-models-that-score/</guid><description>Transformers gains a &quot;decision&quot; task that returns a probability over predefined answers, as a new arXiv paper asks how such models fit into long-horizon agents.</description><pubDate>Tue, 10 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Research digest, week of 10 November 2026</title><link>https://www.aimlfieldguide.com/research/2026-11-10/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/research/2026-11-10/</guid><description>Eight arXiv papers on LoRA capacity, edge weight compression, RAG citation provenance, prompt-injection defences, data-free model merging, output formats for small parsers, SFT versus RL, and CUDA kernels.</description><pubDate>Tue, 10 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Anthropic releases Claude Fable 5.2 and Mythos 5.2 with a paid fast mode</title><link>https://www.aimlfieldguide.com/news/2026-11-09-anthropic-claude-fable-5-2-mythos-5-2/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/news/2026-11-09-anthropic-claude-fable-5-2-mythos-5-2/</guid><description>The two 5.2 models share one underlying network. Anthropic reports faster output, a 2.5 times fast mode at double the price, and about 55 per cent fewer safeguard blocks than Fable 5.1.</description><pubDate>Mon, 09 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Google adds TPU 8tx for confidential compute and the dual-chip TPU 8i Max</title><link>https://www.aimlfieldguide.com/news/2026-11-09-google-tpu-8tx-and-8i-max/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/news/2026-11-09-google-tpu-8tx-and-8i-max/</guid><description>TPU 8tx pairs TPU 8t silicon with AMD EPYC hosts for confidential inference and small-scale training. TPU 8i Max packs two 8i chips into one package. No prices or dates yet.</description><pubDate>Mon, 09 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Ollama 0.40.16 ships Ripple-65B Preview, a dense Apache-2.0 coding model</title><link>https://www.aimlfieldguide.com/news/2026-11-09-ollama-0-40-16-ripple-preview/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/news/2026-11-09-ollama-0-40-16-ripple-preview/</guid><description>Three Ollama releases in a week add Ricursive&apos;s 65 B dense model, MiMo V2.6, YaRN long context for Qwen3, and stop OpenAI-compatible endpoints overriding a model&apos;s sampler defaults.</description><pubDate>Mon, 09 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Moonshot AI publishes Kimi K3.1 Pro weights, a 2.8 T total, 104 B active multimodal MoE</title><link>https://www.aimlfieldguide.com/news/2026-11-05-moonshot-kimi-k3-1-pro-open-weights/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/news/2026-11-05-moonshot-kimi-k3-1-pro-open-weights/</guid><description>The weights ship in MXFP4 under Moonshot&apos;s own licence, which adds a revenue threshold for hosting providers. Context is 1 M tokens and the card recommends vLLM, SGLang or TokenSpeed.</description><pubDate>Thu, 05 Nov 2026 00:00:00 GMT</pubDate></item><item><title>llama.cpp v0.9.0 adds OCP FP8 E4M3 quantisation and a batched tensor copy API</title><link>https://www.aimlfieldguide.com/news/2026-11-04-llama-cpp-0-9-0-fp8-e4m3/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/news/2026-11-04-llama-cpp-0-9-0-fp8-e4m3/</guid><description>The November release brings an FP8 file type, a more efficient unified KV cache for multi-sequence serving, four new architectures and ggml v0.29.0 with initial multi-node tensor parallelism.</description><pubDate>Wed, 04 Nov 2026 00:00:00 GMT</pubDate></item><item><title>Meta buys Conductor, the Mac app that runs Claude Code and Codex agents in parallel</title><link>https://www.aimlfieldguide.com/news/2026-11-04-meta-acquires-conductor/</link><guid isPermaLink="true">https://www.aimlfieldguide.com/news/2026-11-04-meta-acquires-conductor/</guid><description>Terms were not disclosed. Conductor Cloud goes read-only on 13 November and shuts on 20 November, and the local app stops receiving updates in early 2027 while the team joins Meta.</description><pubDate>Wed, 04 Nov 2026 00:00:00 GMT</pubDate></item></channel></rss>