Videos de tokenization
Videos etiquetados con "tokenization"
tokenization 2 videos
Understanding Large Language Models (LLM) | Transformer Architecture, AI Concepts & Future Explained
Discover the complete world of Large Language Models (LLMs) in this detailed educational video designed for students, researchers, AI enthusiasts, educators, developers, and technology learners. In this in-depth session, we explore the latest concepts behind modern Artificial Intelligence systems including Transformer Architecture, Self-Attention Mechanisms, Tokenization, Embeddings, Neural Networks, Context Windows, Retrieval-Augmented Generation (RAG), AI Agents, Multimodal AI, and the future of Generative AI technologies. This educational video explains how modern AI systems process and understand human language using billions of parameters and large-scale datasets. Whether you are beginning your AI journey or already exploring Machine Learning and Natural Language Processing, this video provides a structured and easy-to-understand explanation of the most important concepts behind modern LLMs. The video also covers the evolution from traditional NLP systems to advanced Transformer-based architectures that power today’s AI assistants, intelligent chatbots, research systems, coding assistants, and enterprise AI applications. 📘 Topics Covered in This Video What are Large Language Models (LLMs)? Evolution of NLP and AI Language Systems Transformer Architecture Explained Self-Attention Mechanism Tokens and Tokenization Word Embeddings and Vector Representations Parameters and Neural Networks Training and Fine-Tuning of LLMs Context Windows and Long-Context AI Encoder vs Decoder Models Retrieval-Augmented Generation (RAG) Hallucinations and Limitations of AI AI Safety and Alignment Multimodal AI Systems AI Agents and Autonomous Workflows Latest Trends in Generative AI Real-World Applications of LLMs 🎯 Who Should Watch This Video? This video is highly useful for: Artificial Intelligence Students Machine Learning Enthusiasts NLP Researchers Engineering Students Data Science Learners AI Developers Educators and Teachers Research Scholars Technology Professionals Anyone curious about modern AI systems 🚀 Why This Video Matters Large Language Models are rapidly transforming industries including: Education Healthcare Cybersecurity Software Engineering Scientific Research Business Automation Digital Content Creation Understanding how LLMs work is becoming an essential skill in the modern AI-driven world. This video aims to simplify advanced AI concepts into a structured educational format suitable for learning, teaching, research, and knowledge-building purposes. 📌 Educational Disclaimer This video is created strictly for educational, learning, research, awareness, and knowledge-building purposes only. Some portions of this content are AI-generated and may contain inaccuracies, omissions, outdated information, or unintended errors. Viewers are strongly encouraged to independently verify facts, technical details, research findings, and practical implementations from official and trusted sources before applying them in academic, professional, technical, legal, medical, or commercial environments. This content does not promote misuse of AI technologies and is intended solely for responsible educational understanding. 🔔 Support the Channel If you found this educational AI content useful: Like the video Share with learners and researchers Subscribe for more AI, Machine Learning, NLP, and Technology educational content Enable notifications for future updates #LLM #ArtificialIntelligence #GenerativeAI #MachineLearning #NLP #Transformers #LargeLanguageModels #AI #DeepLearning #NeuralNetworks #AIExplained #DataScience #AI2026 #Technology #EducationalVideo #AIResearch #NotebookLM #FutureOfAI #SelfAttention #RAG Large Language Models, LLM tutorial, What are LLMs, Transformer Architecture, Generative AI, Artificial Intelligence, NLP tutorial, Self Attention Mechanism, AI explained, Machine Learning tutorial, Deep Learning, Neural Networks, AI Agents, Multimodal AI, Retrieval Augmented Generation, RAG systems, Tokenization, Embeddings, Context Window, AI education, NotebookLM content, AI concepts explained, latest AI trends 2026, educational AI video, language models tutorial, ChatGPT concepts, Transformer neural network, AI learning, AI research, Future of AI, AI for students, AI technology explained, Generative AI tutorial, modern AI systems, Large Language Model architecture, NLP concepts, AI knowledge video, educational technology content
How LLMs Understand your Prompts: Tokenization & Embeddings | Chapter 05
What are vector embeddings and tokenization, and how do they let an LLM understand meaning? This video explains tokenization, embeddings, vector dimensions, cosine similarity and positional embeddings - with a hands-on coding demo. ===== In this video, you will learn ===== • What tokenization is and the main types of tokenizers (incl. Byte Pair Encoding) • What vector embeddings are, and what vectors & dimensions actually mean • How an LLM captures meaning using embeddings • How cosine similarity measures how "close" two pieces of text are • Positional embeddings - how models know word order • A hands-on Python demo: tokenizer + embeddings in code This is Part 5 of the GenAI Fundamentals series - for data engineers, developers, and anyone learning how AI language models actually work. ===== Chapters ===== 00:00 - Introduction 00:32 - What is Tokenization and Vector Embeddings? 03:41 - What are Vectors and Dimensions? 06:37 - Why Vectors matter for LLMs? 07:59 - How meaning is captured using Embedding? 12:30 - What is Cosine Similarity? 14:08 - Types of Tokenizers 17:50 - (Hands on) Coding for Tokenizer and Embeddings 26:48 - What are Positional Embeddings? ===== Links ===== Google's "Attention is all You Need" Paper - https://arxiv.org/pdf/1706.03762 Groq Cloud - https://console.groq.com/home GPT Tokenizer - https://platform.openai.com/tokenizer Google Collab - https://colab.research.google.com/ Byte Pair Encoding (BPE) - https://www.geeksforgeeks.org/nlp/byte-pair-encoding-bpe-in-nlp/ Github Code - https://github.com/subhamkharwal/genai-for-data-engineers/blob/master/codes/genai_chap05.ipynb ===== Other Playlists ===== Checkout all other playlists on Data Engineering 👇🏻 https://www.youtube.com/@easewithdata/playlists ===== GitHub Repo ===== https://github.com/subhamkharwal https://github.com/subhamkharwal/genai-for-data-engineers ===== Connect with ME ===== LinkedIn - https://www.linkedin.com/in/subhamkharwal Medium - https://subhamkharwal.medium.com ===== Hashtags ===== #VectorEmbeddings #Tokenization #LLM #GenerativeAI #genai #dataengineering #python #neuralnetworks #machinelearning