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53 articles tagged #Machine Learning.
P.01Meta shipped Muse Glimmer on August 10, an Apache 2.0 model built for local agent tasks that fits on a single 24GB consumer GPU and beats larger rivals on tool-use benchmarks. Here's what it's actually for.
P.02Unit tests don't work on a feature that gives a different answer every time. Evals do. Here's how to build a practical eval harness for an LLM feature, with real code, before it goes anywhere near production.
P.03Meta entered the terminal coding agent market on August 5 with Muse Code, running on the new Muse Spark 1.2 model. It scores 82.9% on Terminal-Bench 2.1, ahead of Codex and Grok Build, behind Claude Code. Here's what actually shipped.
P.04Shieldstral is a 3-billion-parameter open-weight model that judges text and images against safety policies written in plain language at inference time, no retraining required. Here's what that actually changes for teams building moderation.
P.05Alibaba's Qwen3.8-Max launched August 3 with 2.4 trillion parameters, a 1-million-token context window, and a promise to open-source the weights within a week. Here's what actually changes for teams evaluating it against closed frontier models.
P.06Qwen3.7 Flash costs $0.03 per million input tokens and $0.13 per million output tokens, roughly 10x cheaper than Gemini 3.5 Flash-Lite. Here's the honest read on when to actually use it.
P.07MLOps sits between the model-building work of a data scientist and the general infrastructure work of a DevOps engineer. Here's what the role actually owns, what to screen for, and where the candidates come from.
P.08Mistral AI's first robotics release is an 8B vision-language model that moves robots through unfamiliar spaces using one RGB camera and a plain-language instruction, trained only in simulation, no LiDAR or depth sensors required.
P.09Google shipped an open-source Colab CLI that provisions T4, L4, A100, and H100 GPUs (and TPUs) from the command line. Here's how to install it, run your first job, and wire it into a terminal AI agent.
P.10LoRA and QLoRA let you fine-tune a multi-billion-parameter model on a single consumer GPU by training a small set of adapter weights instead of the whole network. Here's how each works and when to pick one over the other.
P.11Google shipped Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber on July 21, 2026, without a 3.5 Pro update. 3.6 Flash cuts output tokens by up to 17%, drops output pricing to $7.50/1M, and pushes computer-use accuracy from 78.4% to 83%. Here's what actually changed and who should care.
P.12Moonshot AI's Kimi K3 launched July 16 as the largest open-weight model yet, and it already leads closed frontier models on several real-world benchmarks. Here's what changed, what it costs, and when it actually matters for your stack.
P.13On June 24, 2026, Qualcomm announced a $3.92B all-stock acquisition of Modular, the company behind the Mojo programming language and MAX inference engine. The bet: give AI developers a hardware-agnostic path that bypasses NVIDIA's CUDA lock-in. Here's what actually changes.
P.14MiniMax M3, released June 1 2026, is the first open-weight model to combine frontier-tier coding performance, a 1-million-token context window, and native multimodality in a single model. Here's what it does, how it benchmarks, and why it matters.
P.15Data scientist and ML engineer are two different jobs. This guide explains the distinction, what a strong data scientist actually does, and what to screen for in the interview to find someone with real analytical judgment.
P.16Everyone claims ML experience since the AI boom. This is the screen that tells apart someone who can build and ship ML systems from someone who fine-tuned a model in a Colab notebook once.
P.17Clients ask agencies to 'do something with these PDFs' more often than you'd think. Here's how to actually build document extraction pipelines that work in production: OCR, vision models, and structured output.
P.18Both approaches customize LLM behavior for your use case, but they solve different problems. Here is how to decide which one you need, how to know when to use both, and what teams consistently get wrong.
P.19Past the chatbot hype, AI is genuinely improving specific e-commerce outcomes. Here are the implementations producing measurable results, and the ones that still mostly disappoint.
P.20Explore how AutoML is accelerating AI development in 2026 with new tools, techniques, and trends that businesses need to know.
P.21A practical guide to deploying Small Language Models (SLMs) like Phi-4, Qwen2.5, Gemma 3, and Llama 3.2 in production. Benchmarks, quantization, edge deployment patterns, and when SLMs beat large models.
P.22EditorPickThe network latency between your Django app and your FastAPI ML service is probably longer than inference itself. Here is how to serve models from Django directly.
P.23Not everything needs the cloud. Edge AI is putting real intelligence on devices, sensors, and cameras — with millisecond latency and zero internet dependency. Here is where the technology actually stands.
P.24DeepSeek and Alibaba's Qwen surged from 1% to 15% global AI market share in a single year. With 700M+ Hugging Face downloads, open-source AI from China is reshaping enterprise choices, developer workflows, and the competitive landscape.
P.25TII's Falcon-H1R 7B scores 88.1% on AIME-24 math, outperforming 15B models. Built on a hybrid Transformer-Mamba architecture, it signals a new era for efficient AI. Here's what it means for developers.
P.26The AI industry is shifting from massive general-purpose models to smaller, specialized ones that outperform giants in specific tasks. Here's why this matters and how to take advantage of it.
P.27The AI industry is pivoting from massive models to efficient SLMs offering 10-30x reductions in latency and cost. Learn why smaller is better and how to leverage SLMs in your applications.
P.28Understand overfitting and underfitting in ML models with practical solutions including cross-validation, regularization, early stopping, and data augmentation.
P.29Explore dimensionality reduction techniques — PCA, LDA, t-SNE, and autoencoders — for improving model performance and data visualization.
P.30Discover the key advantages of Random Forest algorithms — high accuracy, resistance to overfitting, feature importance, and handling missing data.
P.31Master hyperparameter tuning with GridSearchCV using KNN, Random Forest, and SVM models with custom scoring functions.
P.32Build a sentiment analysis pipeline with text preprocessing, TF-IDF vectorization, and Multinomial Naive Bayes classification on Twitter data.
P.33Learn how centroid-based clustering algorithms like K-means partition datasets into meaningful groups based on distance metrics.
P.34A guide to clustering algorithm types — partition-based, hierarchical, density-based, and model-based — with use cases and selection criteria.
P.35Understand decision tree algorithms for classification and regression, their pros and cons, and build an Iris classifier with Python code.
P.36Learn SVM theory including hyperplanes and the kernel trick, then build a classifier on the breast cancer dataset using scikit-learn.
P.37Build a logistic regression model to predict diabetes outcomes using the Pima Indians dataset, covering sigmoid functions, feature scaling, and evaluation.
P.38Implement K-Nearest Neighbors classification using scikit-learn with data visualization, model training, and performance evaluation on real datasets.
P.39Understand the KNN algorithm — how it works, distance metrics, choosing K, and its applications in both classification and regression tasks.
P.40Compare Naive Bayes, SVM, Decision Tree, and Random Forest for email spam detection with a complete Python pipeline from data loading to evaluation.
P.41Learn Occam's Razor, regularization, pruning, ensemble methods, cross-validation, Bayesian model selection, genetic algorithms, and more to boost ML performance.
P.42Master MAE, MSE, R², RMSE, accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrices with formulas and Python code examples.
P.43Understand the bias-variance tradeoff in machine learning with mathematical formulas, visual explanations, and strategies to find the right balance.
P.44Build an insurance cost prediction model using multivariate linear regression with one-hot encoding, evaluation metrics, and residual analysis.
P.45EditorPickMaster 7 encoding techniques for categorical variables — one-hot, label, dummy, binning, count, frequency, and target encoding with Python examples.
P.46Build a linear regression model from scratch using scikit-learn, with data visualization, feature selection, and model evaluation metrics.
P.47Explore reinforcement learning fundamentals — agents, environments, states, actions, Q-learning, SARSA, Actor-Critic, and deep RL approaches.
P.48Understand unsupervised learning methods including clustering, dimensionality reduction, anomaly detection, and generative models with practical examples.
P.49EditorPickA structured 100-day data science bootcamp roadmap covering Python, statistics, machine learning, deep learning, and real-world projects.
P.50EditorPickA comprehensive guide covering 10 regression types — linear, polynomial, logistic, ridge, lasso, elastic net, and more — with Python code examples and selection criteria.
P.51Discover how artificial intelligence and machine learning are transforming augmented and virtual reality applications in gaming, education, and beyond.
P.52Understand the key differences between artificial intelligence, machine learning, and deep learning with clear definitions, examples, and real-world applications.
P.53A step-by-step guide to writing and publishing research papers in artificial intelligence, machine learning, and deep learning — from ideation to submission.