Learn AI Series (#78) - Object Detection (Part 1) - Foundations What will I learn You will learn the leap from classification to localization: not just what, but where; sliding window: the brute force

Learn AI Series (#77) - Image Processing Fundamentals What will I learn You will learn how digital images are represented as numerical arrays: pixels, channels, and data types; color spaces beyond RGB:

Learn AI Series (#76) - Mini Project - Your Own AI Assistant What will I learn You will learn how to combine RAG, agents, tools, and a local model into a working AI assistant; building a domain-specific

Learn AI Series (#75) - Multimodal Models - Text Meets Vision What will I learn You will learn what multimodal means and why combining text and vision unlocks capabilities neither modality achieves alone;

Learn AI Series (#74) - The Hugging Face Ecosystem What will I learn You will learn the Transformers library: models, tokenizers, and pipelines for running inference with one line of code; the Model Hub:

Learn AI Series (#73) - LLM Evaluation What will I learn You will learn why evaluating language models is fundamentally harder than evaluating traditional ML models; perplexity: what it actually measures,

Learn AI Series (#72) - Tokenization Deep Dive What will I learn You will learn why tokenization is one of the most underappreciated decisions in language model design; character-level, word-level, and

Learn AI Series (#71) - Text Generation Techniques What will I learn You will learn how language models actually generate text, one token at a time; greedy decoding, beam search, and sampling strategies;

Learn AI Series (#70) - Running Local Models What will I learn You will learn why running models locally matters: privacy, cost, control, and offline capability; the local inference stack: Ollama, llama.cpp,

Learn AI Series (#69) - Fine-Tuning Language Models What will I learn You will learn when fine-tuning beats prompting and when it doesn't; full fine-tuning vs parameter-efficient methods; LoRA: low-rank

Learn AI Series (#68) - Building AI Agents (Part 2) - Advanced Patterns What will I learn You will learn multi-agent systems: specialized agents that collaborate on complex tasks; hierarchical agents:

Learn AI Series (#67) - Building AI Agents (Part 1) - Foundations What will I learn You will learn what "agents" actually means beyond the marketing hype; the agent loop: observe, think, act,

Learn AI Series (#66) - Working with LLM APIs What will I learn You will learn the LLM provider landscape: OpenAI, Anthropic, Google, and open-source alternatives; the chat completions API: messages, roles,

There are many tokens on Hive-Engine but almost all of them have extremely low trading volume. The biggest problem is that if you buy a large amount of any token, selling it later becomes very difficult

Learn AI Series (#65) - RAG - Advanced Techniques What will I learn You will learn hybrid search -- combining dense vector search with sparse keyword matching for better retrieval; re-ranking with

Learn AI Series (#64) - Retrieval-Augmented Generation (RAG) - Basics What will I learn You will learn why LLMs hallucinate and how RAG addresses the problem; the RAG pipeline: query, retrieve, generate;

Learn AI Series (#63) - Embeddings and Vector Search What will I learn You will learn text embeddings -- representing meaning as dense vectors; sentence embeddings vs word embeddings -- why sentence-level

Learn AI Series (#62) - Prompt Engineering - Getting the Most from LLMs What will I learn You will learn zero-shot, few-shot, and many-shot prompting strategies; chain-of-thought prompting -- making models

Learn AI Series (#61) - Instruction Tuning and Alignment What will I learn You will learn the gap between a pre-trained language model and a useful assistant; supervised fine-tuning (SFT) on

Learn AI Series (#60) - Training Large Language Models What will I learn You will learn what training data goes into LLMs -- Common Crawl, books, code, conversations; data cleaning and deduplication at