#Machine-Learning
13 notas · última:
Mentat will apply for jobs on your behalf and guarantee you get an interview
Mentat, from Y Combinator’s Summer 2016 batch, helps job seekers by optimizing LinkedIn and resumes, writing cover letters, and applying to jobs by hand, with a money-back interview guarantee.
Goodbye Power BI! 📊 In 2025 Build AI/ML Dashboards Entirely Within Python 🤖
The text argues that Taipy is an alternative to Power BI and Tableau for AI/ML dashboards because it keeps work in Python and integrates machine learning libraries and workflows.
Designing Intelligence: System Design in AI/ML Evolution
The piece connects principles such as simplicity, context, resilience, ethics, and decision making to design choices in ML, arguing for useful, interpretable solutions aligned with the business.
3 Ways AI is Contributing to Businesses Success
The text shows how AI, machine learning and automation already help companies with personalized service, more effective email campaigns, and future uses still expanding.
Price per 1M tokens is meaningless
The post argues that per-token pricing is a poor way to compare AI models because tokenizers differ and token efficiency varies. It favors cost per completed task as the more useful metric.
Interviewing for ML/AI Engineers
The essay argues that ML engineer interviews should mix coding, data modeling, system design, project deep dives, and career chats, while limiting ML system design interviews because they often produce low signal.
The AI behind Uplift
Adyen describes the AI stack behind Uplift, from connected decisioning and offline evaluation to weak supervision, transformers, observability, fairness, and explainability in payment flow models.
How Adyen does AI
Adyen says it uses ML on every transaction for fraud, authentication, routing, and conversion, while its GenAI work focuses on support, human-in-the-loop workflows, and open-source models hosted in-house.
Machine Learning for Beginners - A Curriculum
A 12-week, 24-lesson machine learning curriculum focused on classic ML with Scikit-learn, project-based lessons, frequent quizzes, and topics from introduction to reinforcement learning.
Human-Level AI Is Right Around the Corner—or Hundreds of Years Away
IEEE Spectrum collects expert forecasts that range from decades to centuries for human-level AI, with disagreements over timing, what counts as human-like intelligence, and the main risks.
Machine Learning made simple with Ruby
The post shows how to add automatic announcement classification in Ruby with classifier-reborn, choosing an LSI classifier, training it from existing labeled items, and saving predictions back to the database.
Machine Learning: Genetic Algorithms Part 1 (Javascript)
The post introduces genetic algorithms as a way to search for solutions through cost functions, crossover, mutation, and populations, using a Javascript Hello, World example.
The Game of Go: A Programmer's Perspective
The post explains why Go is hard for AI programs and summarizes the methods that improved them: random playouts, bandit algorithms, limited expert knowledge, parallelization, and reinforcement learning.