Build Your AI Knowledge, Comprehensive Courses
for Students & Practitioners
Structured learning paths made to take you from fundamental principles to advanced techniques in modern AI
Most Popular Courses
Implement LLM quantization techniques (PTQ, QAT, GPTQ, GGUF) to reduce model size and improve inference speed.
Approx. 15 hours
LLM Fundamentals & Python
Apply Reinforcement Learning from Human Feedback (RLHF) principles and techniques to align large language models.
Approx. 24 hours
Advanced ML & DL knowledge
Implement and apply advanced reinforcement learning algorithms to solve complex sequential decision-making challenges.
Approx. 70 hours
Python, ML & RL Fundamentals
Build and manage LLM applications using Python, LangChain, LlamaIndex, and essential development practices.
Approx. 18 hours
Intermediate Python skills
Master the theory, mathematics, and implementation of advanced Transformer architectures for modern LLMs.
Approx. 30 hours
Deep Learning & Python Proficiency
Master complex diffusion architectures, advanced training methods, and optimization for cutting-edge generative models.
Approx. 25 hours
Diffusion Model Basics & Python
Acquire the core Python skills needed to write clear, functional code and begin your programming path.
Approx. 20 hours
No prior programming experience.
Master techniques to customize and optimize large language models for specific tasks and domains.
Approx. 28 hours
ML & Transformer Basics
Develop functional AI applications by effectively prompting and integrating Large Language Models.
Approx. 30 hours
Basic Python helpful
Effectively implement, tune, and interpret advanced gradient boosting models for sophisticated machine learning applications.
Approx. 28 hours
Python & ML Fundamentals
Efficiently deploy quantized LLMs on various hardware by mastering advanced techniques and toolkits.
Approx. 22 hours
Python, ML, LLM basics.
Analyze time-dependent data and build statistical forecasting models like ARIMA and SARIMA.
Approx. 15 hours
Basic Python and Pandas
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Detailed material covering theory and practical aspects, suitable for academic study.
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Recent Articles & Insights
May 1, 2025
Stop assuming MoE models automatically mean less VRAM or faster speed locally. Understand the real hardware needs and performance trade-offs for MoE LLMs.
Apr 23, 2025
Accurately estimate the VRAM needed to run or fine-tune Large Language Models. Avoid OOM errors and optimize resource allocation by understanding how model size, precision, batch size, sequence length, and optimization techniques impact GPU memory usage. Includes formulas, code examples, and practical tips.
Apr 18, 2025
Learn 5 key LLM quantization techniques to reduce model size and improve inference speed without significant accuracy loss. Includes technical details and code snippets for engineers.
Apr 18, 2025
Struggling with TensorFlow and NVIDIA GPU compatibility? This guide provides clear steps and tested configurations to help you select the correct TensorFlow, CUDA, and cuDNN versions for optimal performance and stability. Avoid common setup errors and ensure your ML environment is correctly configured.
Apr 18, 2025
Discover the optimal local Large Language Models (LLMs) to run on your NVIDIA RTX 40 series GPU. This guide provides recommendations tailored to each GPU's VRAM (from RTX 4060 to 4090), covering model selection, quantization techniques (GGUF, GPTQ), performance expectations, and essential tools like Ollama, Llama.cpp, and Hugging Face Transformers.
Apr 18, 2025
Learn the practical steps to build and train Mixture of Experts (MoE) models using PyTorch. This guide covers the MoE architecture, gating networks, expert modules, and essential training techniques like load balancing, complete with code examples for machine learning engineers.
Apr 17, 2025
Understand the core differences between LIME and SHAP, two leading model explainability techniques. Learn how each method works, their respective strengths and weaknesses, and practical guidance on when to choose one over the other for interpreting your machine learning models.
Apr 15, 2025
Transformer models can overfit quickly if not properly regularized. This post breaks down practical and effective regularization strategies used in modern transformer architectures, based on research and experience building large-scale models.