#Python

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.

A hands-on series for building a local AI stack with Gemma 4 on Apple Silicon following engineering best practices. From Ollama setup, API integration, RAG pipeline, hybrid retrieval, to observability and hardening for internal environments.

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.

Comprehensive guide to running LLM locally on Mac Apple Silicon (M1/M2/M3/M4) with Ollama and MLX. From initial installation to 3x acceleration with the MLX framework, managing multiple models, integrating APIs into applications, and optimizing GPU/RAM performance. All hands-on, privacy-first, no internet required.

Comprehensive Django course from basic to advanced, helping you master the most powerful full-stack Python framework. Includes Django ORM, Django REST Framework, Class-Based Views, Authentication, Celery, Channels (WebSockets), Testing, Docker and Production deployment. Updated to Django 5.2+ and Python 3.12+ with the latest 2026 best practices.

The Python FastAPI course is comprehensive from basic to advanced, helping you master the most modern Python framework for Backend API. Includes Python fundamentals, Pydantic, async/await, SQLAlchemy, Alembic, Authentication, Authorization, OAuth2, WebSockets, Background Tasks, Testing, Docker and Production deployment. Updated to FastAPI 0.115+ and Python 3.12+ with the latest 2026 best practices.

Comprehensive course on Vibe Coding with GitHub Copilot — from basic concepts to advanced techniques. Learn how to leverage AI Agent, Inline Suggestions, Custom Instructions, MCP Servers, Copilot CLI, and Copilot Coding Agent to accelerate software development. Includes Prompt Engineering for code, security best practices, handling technical debt, building full-stack apps, and professional Vibe Coding processes for production. Updated according to GitHub Copilot 2026 with Agent Mode, Plan Agent, Cloud Agent and the latest features.

Comprehensive course on AI in Healthcare — from Medical Imaging with CNN, NLP for medical records, Drug Discovery with GNN, to implementing HIPAA/FDA compliant AI. Practice with Python, PyTorch, Hugging Face, and standard medical datasets such as MIMIC, CheXpert, PubMed.

Comprehensive course on Generative AI for images and video — from GAN, VAE platforms to Stable Diffusion, DALL-E, Midjourney API. Practice image generation, inpainting, style transfer, video generation, and build Generative AI pipeline production-ready with Python, Hugging Face Diffusers, and ComfyUI.

Comprehensive course on Recommendation Systems — from Collaborative Filtering, Content-based, Matrix Factorization to Deep Learning RecSys with Two-Tower, Graph Neural Networks, Sequence Models. Hands-on with Python, PyTorch, LightFM, and deploy production-ready recommendation engine.

Comprehensive course on Multimodal AI — from Vision-Language Models (CLIP, LLaVA), Visual Question Answering, Image Captioning, to Document AI, Video Understanding. Practice with Python, PyTorch, Hugging Face Transformers, and state-of-the-art models like GPT-4V, Gemini, LLaVA.

Comprehensive course on Natural Language Processing (NLP) — from tokenization platforms, word embeddings, to Transformer architecture, BERT, GPT. Practice text classification, NER, sentiment analysis, machine translation, question answering and build a production-ready NLP pipeline with Python, Hugging Face, and spaCy.

Comprehensive Reinforcement Learning course — from MDP, Q-Learning to Deep RL platforms with DQN, Policy Gradient, PPO, SAC. Practice game AI, robotics simulation, RLHF for LLM, and deploy RL agents production-ready with Python, Gymnasium, Stable-Baselines3.

Comprehensive AI for Speech & Audio course — from audio signal processing, Speech Recognition (ASR) with Whisper, Text-to-Speech (TTS) with VITS, Voice Cloning, Speaker Verification, to Music AI. Practice with Python, PyTorch, Hugging Face, librosa, and state-of-the-art models.

A comprehensive course on Time Series AI — from Statistical Methods (ARIMA, ETS), Machine Learning (XGBoost, LightGBM), to Deep Learning (LSTM, Transformer, TimesFM). Application of forecasting, anomaly detection, classification. Practice with Python, statsmodels, scikit-learn, PyTorch, and specialized frameworks.

The series builds an entire AI system for a Fashion & Print-on-Demand platform — from AI Design Generation (Stable Diffusion, ControlNet), AI Editing in natural language, Personalization System, Virtual Try-On with Computer Vision, to Print File Optimization and AI Product Generation. Each article is an independent AI module that can be deployed in production.

In-depth course on Retrieval-Augmented Generation (RAG) — techniques for connecting the LLM to your own data. From basic RAG to Graph RAG, Agentic RAG, Multimodal RAG. Hands-on with ChromaDB, Qdrant, LangChain, LlamaIndex. Deploy "Chat with Documents" to production.

Comprehensive course on Fine-tuning Large Language Models — when to fine-tune, data preparation, fine-tuning on Google Gemini/Vertex AI, OpenAI, and open-source (LoRA/QLoRA). Compare Fine-tuning vs RAG, model evaluation methods, and production deployment. Calculate actual costs.

Practical course on building AI Agents — from simple chatbots to complex Multi-Agent systems. Proficient in Function Calling, Tool Use, RAG, MCP, LangGraph, CrewAI and deploying Agent to production. Each lesson is coded hands-on with Python.

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.