#Machine Learning

Anthropic launched Claude Opus 4.7 on April 16, 2026 — the latest flagship AI model with exceptional programming capabilities, 3x higher vision resolution, a new xhigh effort level, and leading agentic performance. A comprehensive review covering benchmarks, real-world feedback, pricing, and a migration guide from Opus 4.6.

Duy TranApril 17, 202620 min

Anthropic has published a 245-page System Card for Claude Mythos Preview — the most powerful AI model ever trained, but NOT released publicly due to its ability to autonomously find zero-days. A detailed analysis of its cyber capabilities, alignment, model welfare, and the remarkable stories from inside.

Duy TranApril 13, 202618 min

A comprehensive review of MiniMax — the Chinese AI startup with the world's most complete multimodal ecosystem. From M2.7 (text/code on par with Opus 4.6), Hailuo 2.3 (video), Speech 2.6, to Music 2.6. Analyzes models, products, API, pricing, compares with OpenAI, Google, and Anthropic, and includes a quick-start guide.

Duy TranApril 13, 202625 min

Google DeepMind launches Gemma 4 — a family of open models ranking

Duy TranApril 3, 202615 min

Ollama 0.19 integrates Apple's MLX backend — delivering 93% faster decode speed and 57% faster prefill on M5. A comprehensive technical analysis of unified memory architecture, real-world benchmarks across M1 to M5, and a step-by-step setup guide to maximize your Apple Silicon's potential.

Duy TranApril 1, 202618 min

An in-depth analysis of AI applications in healthcare — from CNN medical imaging that surpasses specialist physicians, NLP for EHR analysis, drug discovery compressed from 12 years to months, to personalized genomics treatment. Includes real case studies, technical challenges, ethical issues, and a deployment roadmap for Vietnam.

Duy TranApril 1, 202635 min

Comprehensive course on AI Agent Engineering — from Python & ML foundation, NLP & LLMs (LLaMA, Mistral, Qwen, Phi), RAG with Vector DB (FAISS, Milvus, Pinecone), to building AI Agent systems with LangChain, LlamaIndex, CrewAI, LangGraph. Practice Fine-tuning, Prompt Engineering, Tool Calling, Multi-Agent. Deploy production with FastAPI, Docker, Microservices, MLOps, CI/CD on AWS/Azure/GCP. Fully meets AI/ML Engineer recruitment requirements at leading technology companies.

Comprehensive study guide for the Google Cloud Professional Machine Learning Engineer exam. Vertex AI, BigQuery ML, TFX pipeline, MLOps on GCP.

An in-depth study guide for the AWS Certified Machine Learning - Specialty (MLS-C01) exam. Master SageMaker, data engineering, modeling, and ML deployment on AWS at the expert level.

Machine Learning roadmap for beginners starts from zero, following an easy-to-understand method: intuition first, code later, just enough math. The course goes from environment setup, first model, proper evaluation, overfitting/data leakage prevention, to common models (Linear, Logistic, Tree, XGBoost), unsupervised, time series, and production deployment. Each cluster of lessons has mini-projects, real-life challenges and a clear output checklist.

In-depth series on Fashion Design & Print-on-Demand (POD) system architecture: domain analysis, AI-powered design studio (Stable Diffusion, ControlNet, CLIP), product catalog & multi-channel e-commerce, order orchestration & fulfillment, supplier network routing, print production pipeline, AI recommendation & trend forecasting, data platform & ML pipeline, Kubernetes infrastructure, security & IP protection, Printful/Printify/Gelato case studies. From design ideas to products in customers' hands.

Comprehensive course on Artificial Intelligence and Large Language Models — from Neural Networks platform, Transformer architecture, to Fine-tuning, RAG, AI Agents and production deployment. Learn hands-on with Python, PyTorch, and LLM APIs.