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Free Download Concept & Coding Llm Transformer,Attention, Deepseek Pytorch Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.28 GB | Duration: 3h 37m How does LLMs works, Understand Concept & Coding of Transformer,Attention, Deepseek using pytorch What you'll learn Learn how attention helps models focus on important text parts. Understand transformers, self-attention, and multi-head attention mechanisms. Explore how LLMs process, tokenize, and generate human-like text. Study DeepSeek's architecture and its optimizations for efficiency. Explore the transformer architecture Requirements python Description Welcome to this comprehensive course on how Large Language Models (LLMs) work! In recent years, LLMs have revolutionized the field of artificial intelligence, powering applications like ChatGPT, DeepSeek, and other advanced AI assistants. But how do these models understand and generate human-like text? In this course, we will break down the fundamental concepts behind LLMs, including attention mechanisms, transformers, and modern architectures like DeepSeek.We will start by exploring the core idea of attention mechanisms, which allow models to focus on the most relevant parts of the input text, improving contextual understanding. Then, we will dive into transformers, the backbone of LLMs, and analyze how they enable efficient parallel processing of text, leading to state-of-the-art performance in natural language processing (NLP). You will also learn about self-attention, positional encodings, and multi-head attention, key components that help models capture long-range dependencies in text.Beyond the basics, we will examine DeepSeek, a cutting-edge open-weight model designed to push the boundaries of AI efficiency and performance. You'll gain insights into how DeepSeek optimizes attention mechanisms and what makes it a strong competitor to other LLMs.By the end of this course, you will have a solid understanding of how LLMs work, how they are trained, and how they can be fine-tuned for specific tasks. Whether you're an AI enthusiast, a developer, or a researcher, this course will equip you with the knowledge to work with and build upon the latest advancements in deep learning and NLP. Let's get started! Overview Section 1: Introduction Lecture 1 Introduction to Course Section 2: Introduction to Transformer Lecture 2 AI History Lecture 3 Language as bag of Words Section 3: Transformer Embedding Lecture 4 Word embedding Lecture 5 Vector Embedding Lecture 6 Types of Embedding Section 4: Transformer -Encoder Decoder context Lecture 7 Encoding Decoding context Lecture 8 Attention Encoder Decoder context Section 5: Transformer Architecture Lecture 9 Transformer Architecture with Attention Lecture 10 GPT vs Bert Model Lecture 11 Context length and number of Parameter Section 6: Transformer -Tokenization code Lecture 12 Tokenization Lecture 13 Code Tokenization Section 7: Transformer model and block Lecture 14 Transformer architecture Lecture 15 Transformer block Section 8: Transformer coding Lecture 16 Decoder Transformer setup and code Lecture 17 Tranformer model download Lecture 18 Transformer model code architecture Lecture 19 Transforme model summary Lecture 20 Transformer code generate token Section 9: Attention-Intro Lecture 21 Transformer attention Lecture 22 Word embedding Lecture 23 Positional encoding Section 10: Attention-Maths Lecture 24 Attention Math Intro Lecture 25 Attention Query,Key,Value example Lecture 26 Attention Q,K,V transformer Lecture 27 Encoded value Lecture 28 Attention formulae Lecture 29 Calculate Q,K transpose Lecture 30 Attention softmax Lecture 31 Why multiply by V in attention Section 11: Attention-code Lecture 32 Attention code Overview Lecture 33 Attention code Lecture 34 Attention code Part2 Section 12: Mask Self Attention Lecture 35 Mask self attention Section 13: Mask Self Attention code Lecture 36 Mask Self Attention code Overview Lecture 37 Mask Self Attention code Section 14: Multimodal Attention Lecture 38 Encoder decoder transformer Lecture 39 Types of Transformer Lecture 40 Multimodal attention Section 15: Multi-Head Attention Lecture 41 Multi-Head Attention Lecture 42 Multi-Head Attention Code Part1 Section 16: Multi-Head Attention code Lecture 43 Multihead attention code Overview Lecture 44 Multi-head attention encoder decoder attention code Section 17: Deepseek R1 and R1-zero Lecture 45 Deepseek R1 training Lecture 46 Deepseek R1-zero Lecture 47 Deepseek R1 Architecture Lecture 48 Deepseek R1 Paper Section 18: Deepseek R1 Paper Lecture 49 Deepseek R1 paper Intro Lecture 50 Deepseek R1 Paper Aha moments Lecture 51 Deepseek R1 Paper Aha moments Part 2 Section 19: Bonus lecture Lecture 52 Deepseek R1 summary Generative AI enthusiasts Homepage: https://www.udemy.com/course/concept-coding-llm-transformerattention-deepseek-pytorch/ DOWNLOAD NOW: Concept & Coding Llm Transformer,Attention, Deepseek Pytorch Rapidgator https://rg.to/file/c88ac55d4c9026906ebe6ba7fd0a1253/cepxf.Concept..Coding.Llm.TransformerAttention.Deepseek.Pytorch.part2.rar.html https://rg.to/file/e558974e0224f11f8a68a7d565f52ad4/cepxf.Concept..Coding.Llm.TransformerAttention.Deepseek.Pytorch.part1.rar.html Fikper Free Download https://fikper.com/5oRB6pz55E/cepxf.Concept..Coding.Llm.TransformerAttention.Deepseek.Pytorch.part1.rar.html https://fikper.com/NozEJeR5MY/cepxf.Concept..Coding.Llm.TransformerAttention.Deepseek.Pytorch.part2.rar.html : No Password - Links are Interchangeable

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In the Verse - Transformer (2016) Title: Transformer Artist : In the Verse Year : 2016 Genre: Alternative rock Packed size: 99 MB Format : MP3 Bitrate : 320Kbps Tracklist : 01 - Compassion 02 - Dying Words 03 - Already Are 04 - Shatter To Pieces 05 - Alone 06 - Your Sacrifice 07 - Transformer 08 - Final Days 09 - Mid-Life's Night 10 - Afterlife http://uploadgig.com/file/download/DCc17DddaCe8e5d4/37374d1437c99140cdb99f3dbe722df64a74b.rar http://rapidgator.net/file/c3aff17836b674e94d18f9957925c0f3/37374d1437c99140cdb99f3dbe722df64a74b.rar.html http://uploaded.net/file/32vj8yyd/37374d1437c99140cdb99f3dbe722df64a74b.rar

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ABBYY PDF Transformer+ 12.0.102.241 Portable | 315.4 MB ABBYY PDF Transformer is a powerful tool that enables you to author and to convert PDF files into multiple editable formats. The program is packed with a lot of features that anyone that works with PDF files can find useful.The most basic action that the program can perform is to create a PDF file from a text document. The program can load TXT, RTF or HTML files and transform them in a PDF file in a couple of clicks. But we have to mention that the HTML files will include the images and styes of the original page, not just the text. Here are some key features of "ABBYY PDF Transformer": Â? Intelligent PDF Conversion Technology Â? Accurate Re-creation of Document Layout and Formatting Â? One-Click PDF Conversion from Windows Explorer Â? Conversion of PDF documents to Editable Formats Â? Manual Area Selection Tools for Advanced Users Â? Flexible Layout Retention Options Â? Conversion of Selected Areas and Pages Â? Print-to-PDF Function from Any Application Â? Retention of Links, Hyperlinks and Metadata Â? Conversion of Scanned PDFs to Searchable PDF files Â? Appending New Files to Existing PDF OS : Microsoft Windows 8 / 7 / Vista / XP Language : English Home Page - http://abbyy.com DOWNLOAD LINKS: (All below links are interchangable - Single Extraction - No password) http://rapidgator.net/file/baad07cfa0a8a7a1f060b81f7c0f5bef/kmn6r.ABBYY.PDF.Transformer.12.0.102.241.Portable.rar.html http://uploaded.net/file/kjdcwl4z/kmn6r.ABBYY.PDF.Transformer.12.0.102.241.Portable.rar http://www.uploadable.ch/file/hGGDV3KwfzgV/kmn6r.ABBYY.PDF.Transformer.12.0.102.241.Portable.rar